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You searched for subject:(dimension reduction). Showing records 1 – 30 of 280 total matches.

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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

 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 (6th 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 (16th Edition):

Thangavelu, Madan Kumar. “On error bounds for linear feature extraction.” 2010. Masters Thesis, Oregon State University. Accessed April 16, 2021. http://hdl.handle.net/1957/13886.

MLA Handbook (7th Edition):

Thangavelu, Madan Kumar. “On error bounds for linear feature extraction.” 2010. Web. 16 Apr 2021.

Vancouver:

Thangavelu MK. On error bounds for linear feature extraction. [Internet] [Masters thesis]. Oregon State University; 2010. [cited 2021 Apr 16]. 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

 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 (6th 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 (16th Edition):

Odom, Gabriel Jairus 1988-. “Three applications of linear dimension reduction.” 2017. Doctoral Dissertation, Baylor University. Accessed April 16, 2021. http://hdl.handle.net/2104/10182.

MLA Handbook (7th Edition):

Odom, Gabriel Jairus 1988-. “Three applications of linear dimension reduction.” 2017. Web. 16 Apr 2021.

Vancouver:

Odom GJ1. Three applications of linear dimension reduction. [Internet] [Doctoral dissertation]. Baylor University; 2017. [cited 2021 Apr 16]. 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

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 (6th 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 (16th Edition):

Coulter, Duncan Anthony. “Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems.” 2014. Thesis, University of Johannesburg. Accessed April 16, 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 (7th Edition):

Coulter, Duncan Anthony. “Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems.” 2014. Web. 16 Apr 2021.

Vancouver:

Coulter DA. Immunologically amplified knowledge and intentions dimensionality reduction in cooperative multi-agent systems. [Internet] [Thesis]. University of Johannesburg; 2014. [cited 2021 Apr 16]. 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Clemson University

4. Knoll, Fiona. Johnson-Lindenstrauss Transformations.

Degree: PhD, Mathematical Sciences, 2017, Clemson University

 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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

Knoll, Fiona. “Johnson-Lindenstrauss Transformations.” 2017. Doctoral Dissertation, Clemson University. Accessed April 16, 2021. https://tigerprints.clemson.edu/all_dissertations/1977.

MLA Handbook (7th Edition):

Knoll, Fiona. “Johnson-Lindenstrauss Transformations.” 2017. Web. 16 Apr 2021.

Vancouver:

Knoll F. Johnson-Lindenstrauss Transformations. [Internet] [Doctoral dissertation]. Clemson University; 2017. [cited 2021 Apr 16]. 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

 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 (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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 April 16, 2021. http://hdl.handle.net/11714/2327.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhou, Yang. “the use of random matrices as a tool for dimension reduction for high dimension reduction in high-dimensional problems.” 2016. Web. 16 Apr 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 Apr 16]. Available from: http://hdl.handle.net/11714/2327.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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

Degree: 2017, University of Waterloo

 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 (6th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Liu, Kai. “Effective Dimensionality Control in Quantitative Finance and Insurance.” 2017. Thesis, University of Waterloo. Accessed April 16, 2021. http://hdl.handle.net/10012/12324.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Liu, Kai. “Effective Dimensionality Control in Quantitative Finance and Insurance.” 2017. Web. 16 Apr 2021.

Vancouver:

Liu K. Effective Dimensionality Control in Quantitative Finance and Insurance. [Internet] [Thesis]. University of Waterloo; 2017. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10012/12324.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Temple University

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

Degree: PhD, 2016, Temple University

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 (6th 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 (16th Edition):

Yang, Chaozheng. “Sufficient Dimension Reduction in Complex Datasets.” 2016. Doctoral Dissertation, Temple University. Accessed April 16, 2021. http://digital.library.temple.edu/u?/p245801coll10,404627.

MLA Handbook (7th Edition):

Yang, Chaozheng. “Sufficient Dimension Reduction in Complex Datasets.” 2016. Web. 16 Apr 2021.

Vancouver:

Yang C. Sufficient Dimension Reduction in Complex Datasets. [Internet] [Doctoral dissertation]. Temple University; 2016. [cited 2021 Apr 16]. 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

 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 (6th 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 (16th Edition):

Chen, Maximillian. “Dimension Reduction And Inferential Procedures For Images.” 2014. Doctoral Dissertation, Cornell University. Accessed April 16, 2021. http://hdl.handle.net/1813/37105.

MLA Handbook (7th Edition):

Chen, Maximillian. “Dimension Reduction And Inferential Procedures For Images.” 2014. Web. 16 Apr 2021.

Vancouver:

Chen M. Dimension Reduction And Inferential Procedures For Images. [Internet] [Doctoral dissertation]. Cornell University; 2014. [cited 2021 Apr 16]. 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

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

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APA (6th 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 (16th Edition):

Pathmanathan, Thinesh. “Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions.” 2018. Masters Thesis, McMaster University. Accessed April 16, 2021. http://hdl.handle.net/11375/22758.

MLA Handbook (7th Edition):

Pathmanathan, Thinesh. “Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions.” 2018. Web. 16 Apr 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 Apr 16]. 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

 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 (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yang, Ching Chi. “Dimensional Analysis for Response Surface Methodology.” 2019. Thesis, Penn State University. Accessed April 16, 2021. https://submit-etda.libraries.psu.edu/catalog/16110cuy130.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yang, Ching Chi. “Dimensional Analysis for Response Surface Methodology.” 2019. Web. 16 Apr 2021.

Vancouver:

Yang CC. Dimensional Analysis for Response Surface Methodology. [Internet] [Thesis]. Penn State University; 2019. [cited 2021 Apr 16]. Available from: https://submit-etda.libraries.psu.edu/catalog/16110cuy130.

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

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

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

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APA (6th 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 (16th Edition):

Santiago, Anna Theresa. “In Silico Comparative Evaluation of Classical and Robust Dimension Reduction for Psychological Assessment.” 2018. Masters Thesis, University of Toronto. Accessed April 16, 2021. http://hdl.handle.net/1807/89519.

MLA Handbook (7th Edition):

Santiago, Anna Theresa. “In Silico Comparative Evaluation of Classical and Robust Dimension Reduction for Psychological Assessment.” 2018. Web. 16 Apr 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 Apr 16]. 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

  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

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APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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 April 16, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Li, Qiongzhu. “Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans.” 2016. Web. 16 Apr 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 Apr 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Technology, Sydney

13. Bian, W. Supervised linear dimension reduction.

Degree: 2012, University of Technology, Sydney

 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.

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APA (6th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Bian, W. “Supervised linear dimension reduction.” 2012. Thesis, University of Technology, Sydney. Accessed April 16, 2021. http://hdl.handle.net/10453/20422.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bian, W. “Supervised linear dimension reduction.” 2012. Web. 16 Apr 2021.

Vancouver:

Bian W. Supervised linear dimension reduction. [Internet] [Thesis]. University of Technology, Sydney; 2012. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10453/20422.

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

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

 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

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APA (6th 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 (16th 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 April 16, 2021. http://hdl.handle.net/2142/98188.

MLA Handbook (7th Edition):

Lee, Chung Eun. “Statistical inference of multivariate time series and functional data using new dependence metrics.” 2017. Web. 16 Apr 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 Apr 16]. 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


University of Waterloo

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

Degree: 2021, University of Waterloo

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

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APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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 April 16, 2021. http://hdl.handle.net/10012/16710.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Xie, Yijun. “Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection.” 2021. Web. 16 Apr 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 Apr 16]. Available from: http://hdl.handle.net/10012/16710.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Virginia Tech

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

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

 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

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APA (6th 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 (16th Edition):

Wenskovitch Jr, John Edward. “Dimension Reduction and Clustering for Interactive Visual Analytics.” 2019. Doctoral Dissertation, Virginia Tech. Accessed April 16, 2021. http://hdl.handle.net/10919/96599.

MLA Handbook (7th Edition):

Wenskovitch Jr, John Edward. “Dimension Reduction and Clustering for Interactive Visual Analytics.” 2019. Web. 16 Apr 2021.

Vancouver:

Wenskovitch Jr JE. Dimension Reduction and Clustering for Interactive Visual Analytics. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Apr 16]. 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 Colorado

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

Degree: PhD, 2018, University of Colorado

  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

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

APA (6th 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 (16th Edition):

Glaws, Andrew Taylor. “Parameter Dimension Reduction for Scientific Computing.” 2018. Doctoral Dissertation, University of Colorado. Accessed April 16, 2021. https://scholar.colorado.edu/csci_gradetds/195.

MLA Handbook (7th Edition):

Glaws, Andrew Taylor. “Parameter Dimension Reduction for Scientific Computing.” 2018. Web. 16 Apr 2021.

Vancouver:

Glaws AT. Parameter Dimension Reduction for Scientific Computing. [Internet] [Doctoral dissertation]. University of Colorado; 2018. [cited 2021 Apr 16]. 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)

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

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

APA (6th 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 (16th 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 April 16, 2021. http://www.theses.fr/2017SACLS029.

MLA Handbook (7th 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. 16 Apr 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 Apr 16]. 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

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

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

APA (6th 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 (16th Edition):

Li, Qingbin. “Online sufficient dimensionality reduction for sequential high-dimensional time-series.” 2015. Masters Thesis, Georgia Tech. Accessed April 16, 2021. http://hdl.handle.net/1853/60385.

MLA Handbook (7th Edition):

Li, Qingbin. “Online sufficient dimensionality reduction for sequential high-dimensional time-series.” 2015. Web. 16 Apr 2021.

Vancouver:

Li Q. Online sufficient dimensionality reduction for sequential high-dimensional time-series. [Internet] [Masters thesis]. Georgia Tech; 2015. [cited 2021 Apr 16]. 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

 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

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

APA (6th 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 (16th Edition):

Wilson, Matthew Robert. “Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.” 2016. Masters Thesis, Clemson University. Accessed April 16, 2021. https://tigerprints.clemson.edu/all_theses/2357.

MLA Handbook (7th Edition):

Wilson, Matthew Robert. “Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.” 2016. Web. 16 Apr 2021.

Vancouver:

Wilson MR. Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality. [Internet] [Masters thesis]. Clemson University; 2016. [cited 2021 Apr 16]. 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

 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

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

APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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 April 16, 2021. https://submit-etda.libraries.psu.edu/catalog/17052dzn112.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Nandy, Debmalya. “COVARIATE INFORMATION: A NOVEL APPROACH TO SUFFICIENT DIMENSION REDUCTION & FEATURE SCREENING FOR ULTRAHIGH-DIMENSIONAL COVARIATES IN SUPERVISED PROBLEMS.” 2019. Web. 16 Apr 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 Apr 16]. Available from: https://submit-etda.libraries.psu.edu/catalog/17052dzn112.

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

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

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

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

APA (6th 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 (16th 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 April 16, 2021. http://www.theses.fr/2014ISAR0010.

MLA Handbook (7th 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. 16 Apr 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 Apr 16]. 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)

 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

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

APA (6th 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 (16th 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 April 16, 2021. http://www.theses.fr/2016SACLX031.

MLA Handbook (7th Edition):

Vu, Khac Ky. “Random projection for high-dimensional optimization : Projection aléatoire pour l'optimisation de grande dimension.” 2016. Web. 16 Apr 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 Apr 16]. 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

 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

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

APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Liu, Kai. “Directional Control of Generating Brownian Path under Quasi Monte Carlo.” 2012. Thesis, University of Waterloo. Accessed April 16, 2021. http://hdl.handle.net/10012/6984.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Liu, Kai. “Directional Control of Generating Brownian Path under Quasi Monte Carlo.” 2012. Web. 16 Apr 2021.

Vancouver:

Liu K. Directional Control of Generating Brownian Path under Quasi Monte Carlo. [Internet] [Thesis]. University of Waterloo; 2012. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10012/6984.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Georgia

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

Degree: 2014, University of Georgia

 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.

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APA (6th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Iaci, Ross J. “Multivariate association and dimension reduction.” 2014. Thesis, University of Georgia. Accessed April 16, 2021. http://hdl.handle.net/10724/24170.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Iaci, Ross J. “Multivariate association and dimension reduction.” 2014. Web. 16 Apr 2021.

Vancouver:

Iaci RJ. Multivariate association and dimension reduction. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10724/24170.

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

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

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

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

APA (6th 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 (16th Edition):

Gaudrie, David. “High-Dimensional Bayesian Multi-Objective Optimization : Optimisation Bayésienne multi-objectif en haute dimension.” 2019. Doctoral Dissertation, Lyon. Accessed April 16, 2021. http://www.theses.fr/2019LYSEM026.

MLA Handbook (7th Edition):

Gaudrie, David. “High-Dimensional Bayesian Multi-Objective Optimization : Optimisation Bayésienne multi-objectif en haute dimension.” 2019. Web. 16 Apr 2021.

Vancouver:

Gaudrie D. High-Dimensional Bayesian Multi-Objective Optimization : Optimisation Bayésienne multi-objectif en haute dimension. [Internet] [Doctoral dissertation]. Lyon; 2019. [cited 2021 Apr 16]. 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)

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

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

APA (6th 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 (16th 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 April 16, 2021. http://www.theses.fr/2016GREAM051.

MLA Handbook (7th 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. 16 Apr 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 Apr 16]. 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

 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

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APA (6th 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 (16th Edition):

Reiss, Randolf H. “Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression.” 2013. Masters Thesis, Texas State University – San Marcos. Accessed April 16, 2021. https://digital.library.txstate.edu/handle/10877/4696.

MLA Handbook (7th Edition):

Reiss, Randolf H. “Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression.” 2013. Web. 16 Apr 2021.

Vancouver:

Reiss RH. Eigenvalues and Eigenvectors in Data Dimension Reduction for Regression. [Internet] [Masters thesis]. Texas State University – San Marcos; 2013. [cited 2021 Apr 16]. 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

 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

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APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Krishnan, Jyothi. “A Cosserat Theory for Solid Crystals – with Application to Fiber-Reinforced Plates.” 2016. Thesis, University of California – Berkeley. Accessed April 16, 2021. http://www.escholarship.org/uc/item/0qt4z77w.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Krishnan, Jyothi. “A Cosserat Theory for Solid Crystals – with Application to Fiber-Reinforced Plates.” 2016. Web. 16 Apr 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 Apr 16]. Available from: http://www.escholarship.org/uc/item/0qt4z77w.

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

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

 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

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APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Albrecht, Georg Hans. “Interactive High Dimensional Data Analysis using the Three Experts.” 2015. Thesis, University of California – Santa Cruz. Accessed April 16, 2021. http://www.escholarship.org/uc/item/58h8g8h2.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Albrecht, Georg Hans. “Interactive High Dimensional Data Analysis using the Three Experts.” 2015. Web. 16 Apr 2021.

Vancouver:

Albrecht GH. Interactive High Dimensional Data Analysis using the Three Experts. [Internet] [Thesis]. University of California – Santa Cruz; 2015. [cited 2021 Apr 16]. Available from: http://www.escholarship.org/uc/item/58h8g8h2.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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