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274 total matches.

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- 2017 – 2021 (98)
- 2012 – 2016 (124)
- 2007 – 2011 (53)
- 2002 – 2006 (12)

Department

- Statistics (19)
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Degrees

- PhD (85)
- Docteur es (45)
- MS (16)

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Oregon State University

1. Thangavelu, Madan Kumar. On error bounds for linear feature extraction.

Degree: MS, Computer Science, 2010, Oregon State University

URL: http://hdl.handle.net/1957/13886

► Linear transformation for *dimension* *reduction* is a well established problem in the field of machine learning. Due to the numerous observability of parameters and data,…
(more)

Subjects/Keywords: Dimension reduction; Dimension reduction (Statistics)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Thangavelu, M. K. (2010). On error bounds for linear feature extraction. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/13886

Chicago Manual of Style (16^{th} Edition):

Thangavelu, Madan Kumar. “On error bounds for linear feature extraction.” 2010. Masters Thesis, Oregon State University. Accessed March 04, 2021. http://hdl.handle.net/1957/13886.

MLA Handbook (7^{th} Edition):

Thangavelu, Madan Kumar. “On error bounds for linear feature extraction.” 2010. Web. 04 Mar 2021.

Vancouver:

Thangavelu MK. On error bounds for linear feature extraction. [Internet] [Masters thesis]. Oregon State University; 2010. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1957/13886.

Council of Science Editors:

Thangavelu MK. On error bounds for linear feature extraction. [Masters Thesis]. Oregon State University; 2010. Available from: http://hdl.handle.net/1957/13886

Baylor University

2.
Odom, Gabriel Jairus. 1988-.
Three applications of linear *dimension* *reduction*.

Degree: PhD, Baylor University. Dept. of Statistical Sciences., 2017, Baylor University

URL: http://hdl.handle.net/2104/10182

► Linear *Dimension* *Reduction* (LDR) has many uses in engineering, business, medicine, economics, data science and others. LDR can be employed when observations are recorded with…
(more)

Subjects/Keywords: Linear dimension reduction.

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APA (6^{th} Edition):

Odom, G. J. 1. (2017). Three applications of linear dimension reduction. (Doctoral Dissertation). Baylor University. Retrieved from http://hdl.handle.net/2104/10182

Chicago Manual of Style (16^{th} Edition):

Odom, Gabriel Jairus 1988-. “Three applications of linear dimension reduction.” 2017. Doctoral Dissertation, Baylor University. Accessed March 04, 2021. http://hdl.handle.net/2104/10182.

MLA Handbook (7^{th} Edition):

Odom, Gabriel Jairus 1988-. “Three applications of linear dimension reduction.” 2017. Web. 04 Mar 2021.

Vancouver:

Odom GJ1. Three applications of linear dimension reduction. [Internet] [Doctoral dissertation]. Baylor University; 2017. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/2104/10182.

Council of Science Editors:

Odom GJ1. Three applications of linear dimension reduction. [Doctoral Dissertation]. Baylor University; 2017. Available from: http://hdl.handle.net/2104/10182

University of Johannesburg

3.
Coulter, Duncan Anthony.
Immunologically amplified knowledge and intentions dimensionality *reduction* in cooperative multi-agent systems.

Degree: 2014, University of Johannesburg

URL: http://hdl.handle.net/10210/12341

►

Ph.D. (Computer Science)

The development of software systems is a relatively recent field of human endeavour. Even so, it has followed a steady progression of… (more)

Subjects/Keywords: Dimension reduction (Statistics); Multiagent systems

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APA (6^{th} Edition):

Coulter, D. A. (2014). Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems. (Thesis). University of Johannesburg. Retrieved from http://hdl.handle.net/10210/12341

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Coulter, Duncan Anthony. “Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems.” 2014. Thesis, University of Johannesburg. Accessed March 04, 2021. http://hdl.handle.net/10210/12341.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Coulter, Duncan Anthony. “Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems.” 2014. Web. 04 Mar 2021.

Vancouver:

Coulter DA. Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems. [Internet] [Thesis]. University of Johannesburg; 2014. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/10210/12341.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Coulter DA. Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems. [Thesis]. University of Johannesburg; 2014. Available from: http://hdl.handle.net/10210/12341

Not specified: Masters Thesis or Doctoral Dissertation

Clemson University

4. Knoll, Fiona. Johnson-Lindenstrauss Transformations.

Degree: PhD, Mathematical Sciences, 2017, Clemson University

URL: https://tigerprints.clemson.edu/all_dissertations/1977

► With the quick progression of technology and the increasing need to process large data, there has been an increased interest in data-dependent and data-independent *dimension*…
(more)

Subjects/Keywords: Data; Dimension Reduction; Johnson-Lindenstrauss

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APA (6^{th} Edition):

Knoll, F. (2017). Johnson-Lindenstrauss Transformations. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/1977

Chicago Manual of Style (16^{th} Edition):

Knoll, Fiona. “Johnson-Lindenstrauss Transformations.” 2017. Doctoral Dissertation, Clemson University. Accessed March 04, 2021. https://tigerprints.clemson.edu/all_dissertations/1977.

MLA Handbook (7^{th} Edition):

Knoll, Fiona. “Johnson-Lindenstrauss Transformations.” 2017. Web. 04 Mar 2021.

Vancouver:

Knoll F. Johnson-Lindenstrauss Transformations. [Internet] [Doctoral dissertation]. Clemson University; 2017. [cited 2021 Mar 04]. Available from: https://tigerprints.clemson.edu/all_dissertations/1977.

Council of Science Editors:

Knoll F. Johnson-Lindenstrauss Transformations. [Doctoral Dissertation]. Clemson University; 2017. Available from: https://tigerprints.clemson.edu/all_dissertations/1977

5.
Zhou, Yang.
the use of random matrices as a tool for *dimension* *reduction* for high *dimension* *reduction* in high-dimensional problems.

Degree: 2016, University of Nevada – Reno

URL: http://hdl.handle.net/11714/2327

► Modern science regularly collects large amounts of high-dimensional data. Examplesof such data are abundant in the biomedical science, geographical sciences,and many other sciences. However, researchers…
(more)

Subjects/Keywords: dimension; high; projection; random; reduction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Zhou, Y. (2016). the use of random matrices as a tool for dimension reduction for high dimension reduction in high-dimensional problems. (Thesis). University of Nevada – Reno. Retrieved from http://hdl.handle.net/11714/2327

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Zhou, Yang. “the use of random matrices as a tool for dimension reduction for high dimension reduction in high-dimensional problems.” 2016. Thesis, University of Nevada – Reno. Accessed March 04, 2021. http://hdl.handle.net/11714/2327.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhou, Yang. “the use of random matrices as a tool for dimension reduction for high dimension reduction in high-dimensional problems.” 2016. Web. 04 Mar 2021.

Vancouver:

Zhou Y. the use of random matrices as a tool for dimension reduction for high dimension reduction in high-dimensional problems. [Internet] [Thesis]. University of Nevada – Reno; 2016. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/11714/2327.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhou Y. the use of random matrices as a tool for dimension reduction for high dimension reduction in high-dimensional problems. [Thesis]. University of Nevada – Reno; 2016. Available from: http://hdl.handle.net/11714/2327

Not specified: Masters Thesis or Doctoral Dissertation

6. Liu, Kai. Effective Dimensionality Control in Quantitative Finance and Insurance.

Degree: 2017, University of Waterloo

URL: http://hdl.handle.net/10012/12324

► It is well-known that *dimension* *reduction* techniques such as the Brownian bridge, principal component analysis, linear transformation could increase the efficiency of Quasi-Monte Carlo (QMC)…
(more)

Subjects/Keywords: QMC; Dimension Reduction; Effective Dimension; Effective Portfolio; Effective Portfolio Dimension

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Liu, K. (2017). Effective Dimensionality Control in Quantitative Finance and Insurance. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/12324

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Liu, Kai. “Effective Dimensionality Control in Quantitative Finance and Insurance.” 2017. Thesis, University of Waterloo. Accessed March 04, 2021. http://hdl.handle.net/10012/12324.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Liu, Kai. “Effective Dimensionality Control in Quantitative Finance and Insurance.” 2017. Web. 04 Mar 2021.

Vancouver:

Liu K. Effective Dimensionality Control in Quantitative Finance and Insurance. [Internet] [Thesis]. University of Waterloo; 2017. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/10012/12324.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu K. Effective Dimensionality Control in Quantitative Finance and Insurance. [Thesis]. University of Waterloo; 2017. Available from: http://hdl.handle.net/10012/12324

Not specified: Masters Thesis or Doctoral Dissertation

Temple University

7.
Yang, Chaozheng.
Sufficient *Dimension* *Reduction* in Complex Datasets.

Degree: PhD, 2016, Temple University

URL: http://digital.library.temple.edu/u?/p245801coll10,404627

►

Statistics

This dissertation focuses on two problems in *dimension* *reduction*. One is using permutation approach to test predictor contribution. The permutation approach applies to marginal…
(more)

Subjects/Keywords: Statistics;

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Yang, C. (2016). Sufficient Dimension Reduction in Complex Datasets. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,404627

Chicago Manual of Style (16^{th} Edition):

Yang, Chaozheng. “Sufficient Dimension Reduction in Complex Datasets.” 2016. Doctoral Dissertation, Temple University. Accessed March 04, 2021. http://digital.library.temple.edu/u?/p245801coll10,404627.

MLA Handbook (7^{th} Edition):

Yang, Chaozheng. “Sufficient Dimension Reduction in Complex Datasets.” 2016. Web. 04 Mar 2021.

Vancouver:

Yang C. Sufficient Dimension Reduction in Complex Datasets. [Internet] [Doctoral dissertation]. Temple University; 2016. [cited 2021 Mar 04]. Available from: http://digital.library.temple.edu/u?/p245801coll10,404627.

Council of Science Editors:

Yang C. Sufficient Dimension Reduction in Complex Datasets. [Doctoral Dissertation]. Temple University; 2016. Available from: http://digital.library.temple.edu/u?/p245801coll10,404627

Cornell University

8.
Chen, Maximillian.
*Dimension**Reduction* And Inferential Procedures For Images.

Degree: PhD, Statistics, 2014, Cornell University

URL: http://hdl.handle.net/1813/37105

► High-dimensional data analysis has been a prominent topic of statistical research in recent years due to the growing presence of high-dimensional electronic data. Much of…
(more)

Subjects/Keywords: imaging data; dimension reduction; hypothesis testing

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APA (6^{th} Edition):

Chen, M. (2014). Dimension Reduction And Inferential Procedures For Images. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/37105

Chicago Manual of Style (16^{th} Edition):

Chen, Maximillian. “Dimension Reduction And Inferential Procedures For Images.” 2014. Doctoral Dissertation, Cornell University. Accessed March 04, 2021. http://hdl.handle.net/1813/37105.

MLA Handbook (7^{th} Edition):

Chen, Maximillian. “Dimension Reduction And Inferential Procedures For Images.” 2014. Web. 04 Mar 2021.

Vancouver:

Chen M. Dimension Reduction And Inferential Procedures For Images. [Internet] [Doctoral dissertation]. Cornell University; 2014. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1813/37105.

Council of Science Editors:

Chen M. Dimension Reduction And Inferential Procedures For Images. [Doctoral Dissertation]. Cornell University; 2014. Available from: http://hdl.handle.net/1813/37105

McMaster University

9.
Pathmanathan, Thinesh.
*Dimension**Reduction* and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions.

Degree: MSc, 2018, McMaster University

URL: http://hdl.handle.net/11375/22758

►

Model-based clustering is a probabilistic approach that views each cluster as a component in an appropriate mixture model. The Gaussian mixture model is one of… (more)

Subjects/Keywords: Model-based clustering; dimension reduction; statistical learning

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Pathmanathan, T. (2018). Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/22758

Chicago Manual of Style (16^{th} Edition):

Pathmanathan, Thinesh. “Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions.” 2018. Masters Thesis, McMaster University. Accessed March 04, 2021. http://hdl.handle.net/11375/22758.

MLA Handbook (7^{th} Edition):

Pathmanathan, Thinesh. “Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions.” 2018. Web. 04 Mar 2021.

Vancouver:

Pathmanathan T. Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/11375/22758.

Council of Science Editors:

Pathmanathan T. Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions. [Masters Thesis]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/22758

Penn State University

10. Yang, Ching Chi. Dimensional Analysis for Response Surface Methodology.

Degree: 2019, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/16110cuy130

► Dimensional analysis is a widely-employed methodology in physics and engineering. Its advantages include, but not limited to: (i) the essential information extraction, (ii) the interpretability…
(more)

Subjects/Keywords: Dimension reduction; Function approximation; Optimization; Variable selection

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APA (6^{th} Edition):

Yang, C. C. (2019). Dimensional Analysis for Response Surface Methodology. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/16110cuy130

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Yang, Ching Chi. “Dimensional Analysis for Response Surface Methodology.” 2019. Thesis, Penn State University. Accessed March 04, 2021. https://submit-etda.libraries.psu.edu/catalog/16110cuy130.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Yang, Ching Chi. “Dimensional Analysis for Response Surface Methodology.” 2019. Web. 04 Mar 2021.

Vancouver:

Yang CC. Dimensional Analysis for Response Surface Methodology. [Internet] [Thesis]. Penn State University; 2019. [cited 2021 Mar 04]. Available from: https://submit-etda.libraries.psu.edu/catalog/16110cuy130.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yang CC. Dimensional Analysis for Response Surface Methodology. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/16110cuy130

Not specified: Masters Thesis or Doctoral Dissertation

University of Toronto

11.
Santiago, Anna Theresa.
In Silico Comparative Evaluation of Classical and Robust *Dimension* *Reduction* for Psychological Assessment.

Degree: 2018, University of Toronto

URL: http://hdl.handle.net/1807/89519

►

The classic exploration of correlated multivariable psychological assessment data employs *dimension* *reduction* of the original p¬ variables to a lower q-dimensional space through principal component…
(more)

Subjects/Keywords: dimension reduction; PCA; projection pursuit; robust; 0308

Record Details Similar Records

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APA (6^{th} Edition):

Santiago, A. T. (2018). In Silico Comparative Evaluation of Classical and Robust Dimension Reduction for Psychological Assessment. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/89519

Chicago Manual of Style (16^{th} Edition):

Santiago, Anna Theresa. “In Silico Comparative Evaluation of Classical and Robust Dimension Reduction for Psychological Assessment.” 2018. Masters Thesis, University of Toronto. Accessed March 04, 2021. http://hdl.handle.net/1807/89519.

MLA Handbook (7^{th} Edition):

Santiago, Anna Theresa. “In Silico Comparative Evaluation of Classical and Robust Dimension Reduction for Psychological Assessment.” 2018. Web. 04 Mar 2021.

Vancouver:

Santiago AT. In Silico Comparative Evaluation of Classical and Robust Dimension Reduction for Psychological Assessment. [Internet] [Masters thesis]. University of Toronto; 2018. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1807/89519.

Council of Science Editors:

Santiago AT. In Silico Comparative Evaluation of Classical and Robust Dimension Reduction for Psychological Assessment. [Masters Thesis]. University of Toronto; 2018. Available from: http://hdl.handle.net/1807/89519

Uppsala University

12. Li, Qiongzhu. Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans.

Degree: Statistics, 2016, Uppsala University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080

► In this paper, we try to compare the performance of two feature *dimension* *reduction* methods, the LASSO and PCA. Both simulation study and empirical…
(more)

Subjects/Keywords: Machine learning; Feature Dimension Reduction; NPL

Record Details Similar Records

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APA (6^{th} Edition):

Li, Q. (2016). Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Li, Qiongzhu. “Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans.” 2016. Thesis, Uppsala University. Accessed March 04, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Li, Qiongzhu. “Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans.” 2016. Web. 04 Mar 2021.

Vancouver:

Li Q. Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans. [Internet] [Thesis]. Uppsala University; 2016. [cited 2021 Mar 04]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li Q. Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans. [Thesis]. Uppsala University; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080

Not specified: Masters Thesis or Doctoral Dissertation

University of Technology, Sydney

13.
Bian, W.
Supervised linear *dimension* * reduction*.

Degree: 2012, University of Technology, Sydney

URL: http://hdl.handle.net/10453/20422

► Supervised linear *dimension* *reduction* (SLDR) is one of the most effective methods for complexity *reduction*, which has been widely applied in pattern recognition, computer vision,…
(more)

Subjects/Keywords: Pattern recognition.; Dimension reduction.; Statistics.; Mathematics.

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Bian, W. (2012). Supervised linear dimension reduction. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/20422

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Bian, W. “Supervised linear dimension reduction.” 2012. Thesis, University of Technology, Sydney. Accessed March 04, 2021. http://hdl.handle.net/10453/20422.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bian, W. “Supervised linear dimension reduction.” 2012. Web. 04 Mar 2021.

Vancouver:

Bian W. Supervised linear dimension reduction. [Internet] [Thesis]. University of Technology, Sydney; 2012. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/10453/20422.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bian W. Supervised linear dimension reduction. [Thesis]. University of Technology, Sydney; 2012. Available from: http://hdl.handle.net/10453/20422

Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

14. Lee, Chung Eun. Statistical inference of multivariate time series and functional data using new dependence metrics.

Degree: PhD, Statistics, 2017, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/98188

► In this thesis, we focus on inference problems for time series and functional data and develop new methodologies by using new dependence metrics which can…
(more)

Subjects/Keywords: Conditional mean; Dimension reduction; Nonlinear dependence

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Lee, C. E. (2017). Statistical inference of multivariate time series and functional data using new dependence metrics. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/98188

Chicago Manual of Style (16^{th} Edition):

Lee, Chung Eun. “Statistical inference of multivariate time series and functional data using new dependence metrics.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed March 04, 2021. http://hdl.handle.net/2142/98188.

MLA Handbook (7^{th} Edition):

Lee, Chung Eun. “Statistical inference of multivariate time series and functional data using new dependence metrics.” 2017. Web. 04 Mar 2021.

Vancouver:

Lee CE. Statistical inference of multivariate time series and functional data using new dependence metrics. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/2142/98188.

Council of Science Editors:

Lee CE. Statistical inference of multivariate time series and functional data using new dependence metrics. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/98188

Virginia Tech

15.
Wenskovitch Jr, John Edward.
*Dimension**Reduction* and Clustering for Interactive Visual Analytics.

Degree: PhD, Computer Science and Applications, 2019, Virginia Tech

URL: http://hdl.handle.net/10919/96599

► When an analyst is exploring a dataset, they seek to gain insight from the data. With data sets growing larger, analysts require techniques to help…
(more)

Subjects/Keywords: Dimension Reduction; Clustering; Semantic Interaction; Visual Analytics

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Wenskovitch Jr, J. E. (2019). Dimension Reduction and Clustering for Interactive Visual Analytics. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/96599

Chicago Manual of Style (16^{th} Edition):

Wenskovitch Jr, John Edward. “Dimension Reduction and Clustering for Interactive Visual Analytics.” 2019. Doctoral Dissertation, Virginia Tech. Accessed March 04, 2021. http://hdl.handle.net/10919/96599.

MLA Handbook (7^{th} Edition):

Wenskovitch Jr, John Edward. “Dimension Reduction and Clustering for Interactive Visual Analytics.” 2019. Web. 04 Mar 2021.

Vancouver:

Wenskovitch Jr JE. Dimension Reduction and Clustering for Interactive Visual Analytics. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/10919/96599.

Council of Science Editors:

Wenskovitch Jr JE. Dimension Reduction and Clustering for Interactive Visual Analytics. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/96599

University of Waterloo

16. Xie, Yijun. Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection.

Degree: 2021, University of Waterloo

URL: http://hdl.handle.net/10012/16710

► *Dimension* *reduction* methods for functional data have been avidly studied in recent years. However, existing methods are primarily based on summarizing the data by their…
(more)

Subjects/Keywords: functional data analysis; dimension reduction; projection pursuit

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Xie, Y. (2021). Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16710

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Xie, Yijun. “Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection.” 2021. Thesis, University of Waterloo. Accessed March 04, 2021. http://hdl.handle.net/10012/16710.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Xie, Yijun. “Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection.” 2021. Web. 04 Mar 2021.

Vancouver:

Xie Y. Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection. [Internet] [Thesis]. University of Waterloo; 2021. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/10012/16710.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xie Y. Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection. [Thesis]. University of Waterloo; 2021. Available from: http://hdl.handle.net/10012/16710

Not specified: Masters Thesis or Doctoral Dissertation

University of Colorado

17.
Glaws, Andrew Taylor.
Parameter *Dimension* *Reduction* for Scientific Computing.

Degree: PhD, 2018, University of Colorado

URL: https://scholar.colorado.edu/csci_gradetds/195

► Advances in computational power have enabled the simulation of increasingly complex physical systems. Mathematically, we represent these simulations as a mapping from inputs to…
(more)

Subjects/Keywords: active subspaces; dimension reduction; magnetohydrodynamics; ridge function; ridge recovery; sufficient dimension reduction; Computer Sciences; Mathematics

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Glaws, A. T. (2018). Parameter Dimension Reduction for Scientific Computing. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/csci_gradetds/195

Chicago Manual of Style (16^{th} Edition):

Glaws, Andrew Taylor. “Parameter Dimension Reduction for Scientific Computing.” 2018. Doctoral Dissertation, University of Colorado. Accessed March 04, 2021. https://scholar.colorado.edu/csci_gradetds/195.

MLA Handbook (7^{th} Edition):

Glaws, Andrew Taylor. “Parameter Dimension Reduction for Scientific Computing.” 2018. Web. 04 Mar 2021.

Vancouver:

Glaws AT. Parameter Dimension Reduction for Scientific Computing. [Internet] [Doctoral dissertation]. University of Colorado; 2018. [cited 2021 Mar 04]. Available from: https://scholar.colorado.edu/csci_gradetds/195.

Council of Science Editors:

Glaws AT. Parameter Dimension Reduction for Scientific Computing. [Doctoral Dissertation]. University of Colorado; 2018. Available from: https://scholar.colorado.edu/csci_gradetds/195

18. Hoyos-Idrobo, Andrés. Ensembles des modeles en fMRI : l'apprentissage stable à grande échelle : Ensembles of models in fMRI : stable learning in large-scale settings.

Degree: Docteur es, Informatique, 2017, Université Paris-Saclay (ComUE)

URL: http://www.theses.fr/2017SACLS029

►

En imagerie médicale, des collaborations internationales ont lançé l'acquisition de centaines de Terabytes de données - et en particulierde données d'Imagerie par Résonance Magnétique fonctionelle… (more)

Subjects/Keywords: IRMf; Clustering; Reduction de dimension; Décodage; FMRI; Clustering; Dimentionality reduction; Decoding

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Hoyos-Idrobo, A. (2017). Ensembles des modeles en fMRI : l'apprentissage stable à grande échelle : Ensembles of models in fMRI : stable learning in large-scale settings. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2017SACLS029

Chicago Manual of Style (16^{th} Edition):

Hoyos-Idrobo, Andrés. “Ensembles des modeles en fMRI : l'apprentissage stable à grande échelle : Ensembles of models in fMRI : stable learning in large-scale settings.” 2017. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed March 04, 2021. http://www.theses.fr/2017SACLS029.

MLA Handbook (7^{th} Edition):

Hoyos-Idrobo, Andrés. “Ensembles des modeles en fMRI : l'apprentissage stable à grande échelle : Ensembles of models in fMRI : stable learning in large-scale settings.” 2017. Web. 04 Mar 2021.

Vancouver:

Hoyos-Idrobo A. Ensembles des modeles en fMRI : l'apprentissage stable à grande échelle : Ensembles of models in fMRI : stable learning in large-scale settings. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2017. [cited 2021 Mar 04]. Available from: http://www.theses.fr/2017SACLS029.

Council of Science Editors:

Hoyos-Idrobo A. Ensembles des modeles en fMRI : l'apprentissage stable à grande échelle : Ensembles of models in fMRI : stable learning in large-scale settings. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2017. Available from: http://www.theses.fr/2017SACLS029

Georgia Tech

19.
Li, Qingbin.
Online sufficient dimensionality *reduction* for sequential high-dimensional time-series.

Degree: MS, Industrial and Systems Engineering, 2015, Georgia Tech

URL: http://hdl.handle.net/1853/60385

In this thesis, we present Online Sufficient Dimensionality Reduction (OSDR) algorithm for real-time high-dimensional sequential data analysis.
*Advisors/Committee Members: Xie, Yao (advisor), Song, Le (committee member), Zhou, Enlu (committee member).*

Subjects/Keywords: Online learning; Dimension reduction; Sufficient dimensionality reduction; Stochastic gradient descent

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Li, Q. (2015). Online sufficient dimensionality reduction for sequential high-dimensional time-series. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60385

Chicago Manual of Style (16^{th} Edition):

Li, Qingbin. “Online sufficient dimensionality reduction for sequential high-dimensional time-series.” 2015. Masters Thesis, Georgia Tech. Accessed March 04, 2021. http://hdl.handle.net/1853/60385.

MLA Handbook (7^{th} Edition):

Li, Qingbin. “Online sufficient dimensionality reduction for sequential high-dimensional time-series.” 2015. Web. 04 Mar 2021.

Vancouver:

Li Q. Online sufficient dimensionality reduction for sequential high-dimensional time-series. [Internet] [Masters thesis]. Georgia Tech; 2015. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1853/60385.

Council of Science Editors:

Li Q. Online sufficient dimensionality reduction for sequential high-dimensional time-series. [Masters Thesis]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/60385

Clemson University

20. Wilson, Matthew Robert. Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.

Degree: MS, Computer Engineering, 2016, Clemson University

URL: https://tigerprints.clemson.edu/all_theses/2357

► Reducing the input dimensionality of large datasets for subsequent processing will allow the process to become less computationally complex and expensive. This thesis tests if…
(more)

Subjects/Keywords: Dimension reduction; feature reduction; feature selection; input reduction; Karnin Sensitivity; Principal Component Analysis

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Wilson, M. R. (2016). Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality. (Masters Thesis). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_theses/2357

Chicago Manual of Style (16^{th} Edition):

Wilson, Matthew Robert. “Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.” 2016. Masters Thesis, Clemson University. Accessed March 04, 2021. https://tigerprints.clemson.edu/all_theses/2357.

MLA Handbook (7^{th} Edition):

Wilson, Matthew Robert. “Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.” 2016. Web. 04 Mar 2021.

Vancouver:

Wilson MR. Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality. [Internet] [Masters thesis]. Clemson University; 2016. [cited 2021 Mar 04]. Available from: https://tigerprints.clemson.edu/all_theses/2357.

Council of Science Editors:

Wilson MR. Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality. [Masters Thesis]. Clemson University; 2016. Available from: https://tigerprints.clemson.edu/all_theses/2357

Penn State University

21.
Nandy, Debmalya.
COVARIATE INFORMATION: A NOVEL APPROACH TO SUFFICIENT *DIMENSION* *REDUCTION* & FEATURE SCREENING FOR ULTRAHIGH-DIMENSIONAL COVARIATES IN SUPERVISED PROBLEMS.

Degree: 2019, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/17052dzn112

► In two major parts as described below, this dissertation presents two novel methods for reducing the *dimension* of the covariate space in large supervised problems.…
(more)

Subjects/Keywords: Fisher information; Density information; Supervised problems; Sufficient dimension reduction; Feature screening; Ultrahigh dimension; Bootstrap

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Nandy, D. (2019). COVARIATE INFORMATION: A NOVEL APPROACH TO SUFFICIENT DIMENSION REDUCTION & FEATURE SCREENING FOR ULTRAHIGH-DIMENSIONAL COVARIATES IN SUPERVISED PROBLEMS. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/17052dzn112

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Nandy, Debmalya. “COVARIATE INFORMATION: A NOVEL APPROACH TO SUFFICIENT DIMENSION REDUCTION & FEATURE SCREENING FOR ULTRAHIGH-DIMENSIONAL COVARIATES IN SUPERVISED PROBLEMS.” 2019. Thesis, Penn State University. Accessed March 04, 2021. https://submit-etda.libraries.psu.edu/catalog/17052dzn112.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Nandy, Debmalya. “COVARIATE INFORMATION: A NOVEL APPROACH TO SUFFICIENT DIMENSION REDUCTION & FEATURE SCREENING FOR ULTRAHIGH-DIMENSIONAL COVARIATES IN SUPERVISED PROBLEMS.” 2019. Web. 04 Mar 2021.

Vancouver:

Nandy D. COVARIATE INFORMATION: A NOVEL APPROACH TO SUFFICIENT DIMENSION REDUCTION & FEATURE SCREENING FOR ULTRAHIGH-DIMENSIONAL COVARIATES IN SUPERVISED PROBLEMS. [Internet] [Thesis]. Penn State University; 2019. [cited 2021 Mar 04]. Available from: https://submit-etda.libraries.psu.edu/catalog/17052dzn112.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nandy D. COVARIATE INFORMATION: A NOVEL APPROACH TO SUFFICIENT DIMENSION REDUCTION & FEATURE SCREENING FOR ULTRAHIGH-DIMENSIONAL COVARIATES IN SUPERVISED PROBLEMS. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/17052dzn112

Not specified: Masters Thesis or Doctoral Dissertation

22.
Lu, Weizhi.
Contribution to *dimension* *reduction* techniques : application to object tracking : Contribution aux techniques de la réduction de *dimension* : application au suivi d'objet.

Degree: Docteur es, Traitement du signal et de l'image, 2014, Rennes, INSA

URL: http://www.theses.fr/2014ISAR0010

►

Cette thèse étudie et apporte des améliorations significatives sur trois techniques répandues en réduction de *dimension* : l'acquisition parcimonieuse (ou l'échantillonnage parcimonieux), la projection aléatoire…
(more)

Subjects/Keywords: Réduction de dimension; Dimension reduction; Compressed sensing; Random projection; Sparse representation; 621.382

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Lu, W. (2014). Contribution to dimension reduction techniques : application to object tracking : Contribution aux techniques de la réduction de dimension : application au suivi d'objet. (Doctoral Dissertation). Rennes, INSA. Retrieved from http://www.theses.fr/2014ISAR0010

Chicago Manual of Style (16^{th} Edition):

Lu, Weizhi. “Contribution to dimension reduction techniques : application to object tracking : Contribution aux techniques de la réduction de dimension : application au suivi d'objet.” 2014. Doctoral Dissertation, Rennes, INSA. Accessed March 04, 2021. http://www.theses.fr/2014ISAR0010.

MLA Handbook (7^{th} Edition):

Lu, Weizhi. “Contribution to dimension reduction techniques : application to object tracking : Contribution aux techniques de la réduction de dimension : application au suivi d'objet.” 2014. Web. 04 Mar 2021.

Vancouver:

Lu W. Contribution to dimension reduction techniques : application to object tracking : Contribution aux techniques de la réduction de dimension : application au suivi d'objet. [Internet] [Doctoral dissertation]. Rennes, INSA; 2014. [cited 2021 Mar 04]. Available from: http://www.theses.fr/2014ISAR0010.

Council of Science Editors:

Lu W. Contribution to dimension reduction techniques : application to object tracking : Contribution aux techniques de la réduction de dimension : application au suivi d'objet. [Doctoral Dissertation]. Rennes, INSA; 2014. Available from: http://www.theses.fr/2014ISAR0010

23.
Vu, Khac Ky.
Random projection for high-dimensional optimization : Projection aléatoire pour l'optimisation de grande * dimension*.

Degree: Docteur es, Informatique, 2016, Université Paris-Saclay (ComUE)

URL: http://www.theses.fr/2016SACLX031

► À l'ère de la numérisation, les données devient pas cher et facile à obtenir. Cela se traduit par de nombreux nouveaux problèmes d'optimisation avec de…
(more)

Subjects/Keywords: Réduction de dimension; Approximation; Optimisation; Algorithmes randomisés; Dimension reduction; Approximation; Optimization; Randomized algorithms

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Vu, K. K. (2016). Random projection for high-dimensional optimization : Projection aléatoire pour l'optimisation de grande dimension. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2016SACLX031

Chicago Manual of Style (16^{th} Edition):

Vu, Khac Ky. “Random projection for high-dimensional optimization : Projection aléatoire pour l'optimisation de grande dimension.” 2016. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed March 04, 2021. http://www.theses.fr/2016SACLX031.

MLA Handbook (7^{th} Edition):

Vu, Khac Ky. “Random projection for high-dimensional optimization : Projection aléatoire pour l'optimisation de grande dimension.” 2016. Web. 04 Mar 2021.

Vancouver:

Vu KK. Random projection for high-dimensional optimization : Projection aléatoire pour l'optimisation de grande dimension. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2016. [cited 2021 Mar 04]. Available from: http://www.theses.fr/2016SACLX031.

Council of Science Editors:

Vu KK. Random projection for high-dimensional optimization : Projection aléatoire pour l'optimisation de grande dimension. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2016. Available from: http://www.theses.fr/2016SACLX031

University of Waterloo

24. Liu, Kai. Directional Control of Generating Brownian Path under Quasi Monte Carlo.

Degree: 2012, University of Waterloo

URL: http://hdl.handle.net/10012/6984

► Quasi-Monte Carlo (QMC) methods are playing an increasingly important role in computational finance. This is attributed to the increased complexity of the derivative securities and…
(more)

Subjects/Keywords: QMC; Low Discrepancy Sequence; Effective Dimension; Dimension Reduction; PCA; BB; LT; OT; FOT; DC

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Liu, K. (2012). Directional Control of Generating Brownian Path under Quasi Monte Carlo. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/6984

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Liu, Kai. “Directional Control of Generating Brownian Path under Quasi Monte Carlo.” 2012. Thesis, University of Waterloo. Accessed March 04, 2021. http://hdl.handle.net/10012/6984.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Liu, Kai. “Directional Control of Generating Brownian Path under Quasi Monte Carlo.” 2012. Web. 04 Mar 2021.

Vancouver:

Liu K. Directional Control of Generating Brownian Path under Quasi Monte Carlo. [Internet] [Thesis]. University of Waterloo; 2012. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/10012/6984.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu K. Directional Control of Generating Brownian Path under Quasi Monte Carlo. [Thesis]. University of Waterloo; 2012. Available from: http://hdl.handle.net/10012/6984

Not specified: Masters Thesis or Doctoral Dissertation

University of Georgia

25.
Iaci, Ross J.
Multivariate association and *dimension* * reduction*.

Degree: 2014, University of Georgia

URL: http://hdl.handle.net/10724/24170

► In this thesis, two different nonparametric methods are developed in the statistical field of multivariate association and *dimension* *reduction*.While the underlying goal in both methods…
(more)

Subjects/Keywords: Information variates; Kernel density estimators; Modules; Permutation test; Dimension reduction; Canonical Correlation Analysis; Projection pursuit; Bootstrapping; Dimension reduction; Single index model.

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Iaci, R. J. (2014). Multivariate association and dimension reduction. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/24170

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Iaci, Ross J. “Multivariate association and dimension reduction.” 2014. Thesis, University of Georgia. Accessed March 04, 2021. http://hdl.handle.net/10724/24170.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Iaci, Ross J. “Multivariate association and dimension reduction.” 2014. Web. 04 Mar 2021.

Vancouver:

Iaci RJ. Multivariate association and dimension reduction. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/10724/24170.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Iaci RJ. Multivariate association and dimension reduction. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/24170

Not specified: Masters Thesis or Doctoral Dissertation

26.
Gaudrie, David.
High-Dimensional Bayesian Multi-Objective Optimization : Optimisation Bayésienne multi-objectif en haute * dimension*.

Degree: Docteur es, Mathématiques appliquées, 2019, Lyon

URL: http://www.theses.fr/2019LYSEM026

►

Dans cette thèse, nous nous intéressons à l'optimisation simultanée de fonctions coûteuses à évaluer et dépendant d'un grand nombre de paramètres. Cette situation est rencontrée… (more)

Subjects/Keywords: Optimisation de forme; Processus gaussiens; Optimisation multi-objectif; Reduction de dimension; Gaussian Processes; Bayesian Optimization; Multi-Objective Optimization; Dimension Reduction

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Gaudrie, D. (2019). High-Dimensional Bayesian Multi-Objective Optimization : Optimisation Bayésienne multi-objectif en haute dimension. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2019LYSEM026

Chicago Manual of Style (16^{th} Edition):

Gaudrie, David. “High-Dimensional Bayesian Multi-Objective Optimization : Optimisation Bayésienne multi-objectif en haute dimension.” 2019. Doctoral Dissertation, Lyon. Accessed March 04, 2021. http://www.theses.fr/2019LYSEM026.

MLA Handbook (7^{th} Edition):

Gaudrie, David. “High-Dimensional Bayesian Multi-Objective Optimization : Optimisation Bayésienne multi-objectif en haute dimension.” 2019. Web. 04 Mar 2021.

Vancouver:

Gaudrie D. High-Dimensional Bayesian Multi-Objective Optimization : Optimisation Bayésienne multi-objectif en haute dimension. [Internet] [Doctoral dissertation]. Lyon; 2019. [cited 2021 Mar 04]. Available from: http://www.theses.fr/2019LYSEM026.

Council of Science Editors:

Gaudrie D. High-Dimensional Bayesian Multi-Objective Optimization : Optimisation Bayésienne multi-objectif en haute dimension. [Doctoral Dissertation]. Lyon; 2019. Available from: http://www.theses.fr/2019LYSEM026

27.
Chiancone, Alessandro.
Réduction de *dimension* via Sliced Inverse Regression : Idées et nouvelles propositions : *Dimension* reductio via Sliced Inverse Regression : ideas and extensions.

Degree: Docteur es, Mathématiques Appliquées, 2016, Université Grenoble Alpes (ComUE)

URL: http://www.theses.fr/2016GREAM051

►

Cette thèse propose trois extensions de la Régression linéaire par tranches (Sliced Inverse Regression, SIR), notamment Collaborative SIR, Student SIR et Knockoff SIR.Une des faiblesses… (more)

Subjects/Keywords: Régression linéaire par tranches; Reduction de dimension; Selection de variables; Sliced Inverse Regression; Dimension reduction; Variable selection; 510

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Chiancone, A. (2016). Réduction de dimension via Sliced Inverse Regression : Idées et nouvelles propositions : Dimension reductio via Sliced Inverse Regression : ideas and extensions. (Doctoral Dissertation). Université Grenoble Alpes (ComUE). Retrieved from http://www.theses.fr/2016GREAM051

Chicago Manual of Style (16^{th} Edition):

Chiancone, Alessandro. “Réduction de dimension via Sliced Inverse Regression : Idées et nouvelles propositions : Dimension reductio via Sliced Inverse Regression : ideas and extensions.” 2016. Doctoral Dissertation, Université Grenoble Alpes (ComUE). Accessed March 04, 2021. http://www.theses.fr/2016GREAM051.

MLA Handbook (7^{th} Edition):

Chiancone, Alessandro. “Réduction de dimension via Sliced Inverse Regression : Idées et nouvelles propositions : Dimension reductio via Sliced Inverse Regression : ideas and extensions.” 2016. Web. 04 Mar 2021.

Vancouver:

Chiancone A. Réduction de dimension via Sliced Inverse Regression : Idées et nouvelles propositions : Dimension reductio via Sliced Inverse Regression : ideas and extensions. [Internet] [Doctoral dissertation]. Université Grenoble Alpes (ComUE); 2016. [cited 2021 Mar 04]. Available from: http://www.theses.fr/2016GREAM051.

Council of Science Editors:

Chiancone A. Réduction de dimension via Sliced Inverse Regression : Idées et nouvelles propositions : Dimension reductio via Sliced Inverse Regression : ideas and extensions. [Doctoral Dissertation]. Université Grenoble Alpes (ComUE); 2016. Available from: http://www.theses.fr/2016GREAM051

Texas State University – San Marcos

28.
Reiss, Randolf H.
Eigenvalues and Eigenvectors in Data *Dimension* *Reduction* for Regression.

Degree: MS, Mathematics, 2013, Texas State University – San Marcos

URL: https://digital.library.txstate.edu/handle/10877/4696

► A basic theory of eigenvalues and eigenvectors as a means to reduce the *dimension* of data, is presented. Iterative methods for finding eigenvalues and eigenvectors…
(more)

Subjects/Keywords: Eigenvector; Eigenvalue; Dimension reduction; Power method; Partial least squares; Eigenvalues; Eigenvectors; Data reduction

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Reiss, R. H. (2013). Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/4696

Chicago Manual of Style (16^{th} Edition):

Reiss, Randolf H. “Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression.” 2013. Masters Thesis, Texas State University – San Marcos. Accessed March 04, 2021. https://digital.library.txstate.edu/handle/10877/4696.

MLA Handbook (7^{th} Edition):

Reiss, Randolf H. “Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression.” 2013. Web. 04 Mar 2021.

Vancouver:

Reiss RH. Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression. [Internet] [Masters thesis]. Texas State University – San Marcos; 2013. [cited 2021 Mar 04]. Available from: https://digital.library.txstate.edu/handle/10877/4696.

Council of Science Editors:

Reiss RH. Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression. [Masters Thesis]. Texas State University – San Marcos; 2013. Available from: https://digital.library.txstate.edu/handle/10877/4696

University of California – Berkeley

29. Krishnan, Jyothi. A Cosserat Theory for Solid Crystals – with Application to Fiber-Reinforced Plates.

Degree: Mechanical Engineering, 2016, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/0qt4z77w

► The focus of this thesis is to understand the behavior of composite plates reinforced withrigid bars that are free to twist and bend with respect…
(more)

Subjects/Keywords: Mechanical engineering; Applied mathematics; Cosserat; Dimension Reduction; Fiber; Plate

Record Details Similar Records

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APA (6^{th} Edition):

Krishnan, J. (2016). A Cosserat Theory for Solid Crystals – with Application to Fiber-Reinforced Plates. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/0qt4z77w

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Krishnan, Jyothi. “A Cosserat Theory for Solid Crystals – with Application to Fiber-Reinforced Plates.” 2016. Thesis, University of California – Berkeley. Accessed March 04, 2021. http://www.escholarship.org/uc/item/0qt4z77w.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Krishnan, Jyothi. “A Cosserat Theory for Solid Crystals – with Application to Fiber-Reinforced Plates.” 2016. Web. 04 Mar 2021.

Vancouver:

Krishnan J. A Cosserat Theory for Solid Crystals – with Application to Fiber-Reinforced Plates. [Internet] [Thesis]. University of California – Berkeley; 2016. [cited 2021 Mar 04]. Available from: http://www.escholarship.org/uc/item/0qt4z77w.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Krishnan J. A Cosserat Theory for Solid Crystals – with Application to Fiber-Reinforced Plates. [Thesis]. University of California – Berkeley; 2016. Available from: http://www.escholarship.org/uc/item/0qt4z77w

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Santa Cruz

30. Albrecht, Georg Hans. Interactive High Dimensional Data Analysis using the Three Experts.

Degree: Computer Science, 2015, University of California – Santa Cruz

URL: http://www.escholarship.org/uc/item/58h8g8h2

► With the increasing availability of different kinds of data from various domains such as health care, finance, social networks, etc. there is a need to…
(more)

Subjects/Keywords: Computer science; dimension reduction; high dimesion; interactive; interface; three experts; visualization

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Albrecht, G. H. (2015). Interactive High Dimensional Data Analysis using the Three Experts. (Thesis). University of California – Santa Cruz. Retrieved from http://www.escholarship.org/uc/item/58h8g8h2

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Albrecht, Georg Hans. “Interactive High Dimensional Data Analysis using the Three Experts.” 2015. Thesis, University of California – Santa Cruz. Accessed March 04, 2021. http://www.escholarship.org/uc/item/58h8g8h2.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Albrecht, Georg Hans. “Interactive High Dimensional Data Analysis using the Three Experts.” 2015. Web. 04 Mar 2021.

Vancouver:

Albrecht GH. Interactive High Dimensional Data Analysis using the Three Experts. [Internet] [Thesis]. University of California – Santa Cruz; 2015. [cited 2021 Mar 04]. Available from: http://www.escholarship.org/uc/item/58h8g8h2.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Albrecht GH. Interactive High Dimensional Data Analysis using the Three Experts. [Thesis]. University of California – Santa Cruz; 2015. Available from: http://www.escholarship.org/uc/item/58h8g8h2

Not specified: Masters Thesis or Doctoral Dissertation