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132 total matches.

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Dates

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- 2011 – 2015 (58)
- 2006 – 2010 (23)

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- MS (12)
- Docteur es (10)

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Penn State University

1. Chatterjee, Anirban. Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery.

Degree: PhD, Computer Science and Engineering, 2011, Penn State University

URL: https://etda.libraries.psu.edu/catalog/12026

► *Data*-driven discovery seeks to obtain a computational model of the underlying process using observed *data* on a large number of variables. Observations can be viewed…
(more)

Subjects/Keywords: sparse graph embedding; sparse graph partitioning; data mining; sparse linear solvers

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

APA (6^{th} Edition):

Chatterjee, A. (2011). Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/12026

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

Chatterjee, Anirban. “Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery.” 2011. Doctoral Dissertation, Penn State University. Accessed January 18, 2020. https://etda.libraries.psu.edu/catalog/12026.

MLA Handbook (7^{th} Edition):

Chatterjee, Anirban. “Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery.” 2011. Web. 18 Jan 2020.

Vancouver:

Chatterjee A. Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery. [Internet] [Doctoral dissertation]. Penn State University; 2011. [cited 2020 Jan 18]. Available from: https://etda.libraries.psu.edu/catalog/12026.

Council of Science Editors:

Chatterjee A. Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery. [Doctoral Dissertation]. Penn State University; 2011. Available from: https://etda.libraries.psu.edu/catalog/12026

University of Hong Kong

2.
Li, Mingfei.
* Sparse* representation and fast processing of massive

Degree: M. Phil., 2012, University of Hong Kong

URL: Li, M. [李明飞]. (2012). Sparse representation and fast processing of massive data. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4961797 ; http://dx.doi.org/10.5353/th_b4961797 ; http://hdl.handle.net/10722/181480

►

Many computational problems involve massive *data*. A reasonable solution to those problems should be able to store and process the *data* in a effective manner.…
(more)

Subjects/Keywords: Data mining.; Sparse matrices.

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APA (6^{th} Edition):

Li, M. (2012). Sparse representation and fast processing of massive data. (Masters Thesis). University of Hong Kong. Retrieved from Li, M. [李明飞]. (2012). Sparse representation and fast processing of massive data. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4961797 ; http://dx.doi.org/10.5353/th_b4961797 ; http://hdl.handle.net/10722/181480

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

Li, Mingfei. “Sparse representation and fast processing of massive data.” 2012. Masters Thesis, University of Hong Kong. Accessed January 18, 2020. Li, M. [李明飞]. (2012). Sparse representation and fast processing of massive data. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4961797 ; http://dx.doi.org/10.5353/th_b4961797 ; http://hdl.handle.net/10722/181480.

MLA Handbook (7^{th} Edition):

Li, Mingfei. “Sparse representation and fast processing of massive data.” 2012. Web. 18 Jan 2020.

Vancouver:

Li M. Sparse representation and fast processing of massive data. [Internet] [Masters thesis]. University of Hong Kong; 2012. [cited 2020 Jan 18]. Available from: Li, M. [李明飞]. (2012). Sparse representation and fast processing of massive data. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4961797 ; http://dx.doi.org/10.5353/th_b4961797 ; http://hdl.handle.net/10722/181480.

Council of Science Editors:

Li M. Sparse representation and fast processing of massive data. [Masters Thesis]. University of Hong Kong; 2012. Available from: Li, M. [李明飞]. (2012). Sparse representation and fast processing of massive data. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4961797 ; http://dx.doi.org/10.5353/th_b4961797 ; http://hdl.handle.net/10722/181480

University of Southern California

3.
Lin, Yenting.
Transmission tomography for high contrast media based on
*sparse* * data*.

Degree: PhD, Electrical Engineering, 2013, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/315223/rec/7584

► Transmission tomography is a powerful tool to image the interior structure based on measured *data* on the boundary. It provides a "non-destructive" imaging and widely…
(more)

Subjects/Keywords: tomography; high contrast; sparse data

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APA (6^{th} Edition):

Lin, Y. (2013). Transmission tomography for high contrast media based on sparse data. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/315223/rec/7584

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

Lin, Yenting. “Transmission tomography for high contrast media based on sparse data.” 2013. Doctoral Dissertation, University of Southern California. Accessed January 18, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/315223/rec/7584.

MLA Handbook (7^{th} Edition):

Lin, Yenting. “Transmission tomography for high contrast media based on sparse data.” 2013. Web. 18 Jan 2020.

Vancouver:

Lin Y. Transmission tomography for high contrast media based on sparse data. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2020 Jan 18]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/315223/rec/7584.

Council of Science Editors:

Lin Y. Transmission tomography for high contrast media based on sparse data. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/315223/rec/7584

Texas A&M University

4.
Ren, Shaogang.
SCALABLE ALGORITHMS FOR HIGH DIMENSIONAL STRUCTURED * DATA*.

Degree: PhD, Computer Engineering, 2017, Texas A&M University

URL: http://hdl.handle.net/1969.1/173033

► Emerging technologies and digital devices provide us with increasingly large volume of *data* with respect to both the sample size and the number of features.…
(more)

Subjects/Keywords: Sparse Learning; LASSO; Structured Sparse; Scalability; Big Data

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APA (6^{th} Edition):

Ren, S. (2017). SCALABLE ALGORITHMS FOR HIGH DIMENSIONAL STRUCTURED DATA. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/173033

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

Ren, Shaogang. “SCALABLE ALGORITHMS FOR HIGH DIMENSIONAL STRUCTURED DATA.” 2017. Doctoral Dissertation, Texas A&M University. Accessed January 18, 2020. http://hdl.handle.net/1969.1/173033.

MLA Handbook (7^{th} Edition):

Ren, Shaogang. “SCALABLE ALGORITHMS FOR HIGH DIMENSIONAL STRUCTURED DATA.” 2017. Web. 18 Jan 2020.

Vancouver:

Ren S. SCALABLE ALGORITHMS FOR HIGH DIMENSIONAL STRUCTURED DATA. [Internet] [Doctoral dissertation]. Texas A&M University; 2017. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/1969.1/173033.

Council of Science Editors:

Ren S. SCALABLE ALGORITHMS FOR HIGH DIMENSIONAL STRUCTURED DATA. [Doctoral Dissertation]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/173033

University of Lethbridge

5.
Hasan, Mahmudul.
DSJM : a software toolkit for direct determination of *sparse* Jacobian matrices
.

Degree: 2011, University of Lethbridge

URL: http://hdl.handle.net/10133/3216

► DSJM is a software toolkit written in portable C++ that enables direct determination of *sparse* Jacobian matrices whose sparsity pattern is a priori known. Using…
(more)

Subjects/Keywords: Sparse matrices; Sparse matrices – Computer programs; Jacobians – Data processing; Dissertations, Academic

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

APA (6^{th} Edition):

Hasan, M. (2011). DSJM : a software toolkit for direct determination of sparse Jacobian matrices . (Thesis). University of Lethbridge. Retrieved from http://hdl.handle.net/10133/3216

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):

Hasan, Mahmudul. “DSJM : a software toolkit for direct determination of sparse Jacobian matrices .” 2011. Thesis, University of Lethbridge. Accessed January 18, 2020. http://hdl.handle.net/10133/3216.

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Hasan, Mahmudul. “DSJM : a software toolkit for direct determination of sparse Jacobian matrices .” 2011. Web. 18 Jan 2020.

Vancouver:

Hasan M. DSJM : a software toolkit for direct determination of sparse Jacobian matrices . [Internet] [Thesis]. University of Lethbridge; 2011. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/10133/3216.

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

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hasan M. DSJM : a software toolkit for direct determination of sparse Jacobian matrices . [Thesis]. University of Lethbridge; 2011. Available from: http://hdl.handle.net/10133/3216

Not specified: Masters Thesis or Doctoral Dissertation

Georgia Tech

6.
Whitaker, Bradley M.
Modifying *sparse* coding to model imbalanced datasets.

Degree: PhD, Electrical and Computer Engineering, 2018, Georgia Tech

URL: http://hdl.handle.net/1853/59919

► The objective of this research is to explore the use of *sparse* coding as a tool for unsupervised feature learning to more effectively model imbalanced…
(more)

Subjects/Keywords: Sparse coding; Imbalanced data; Machine learning

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

APA (6^{th} Edition):

Whitaker, B. M. (2018). Modifying sparse coding to model imbalanced datasets. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59919

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

Whitaker, Bradley M. “Modifying sparse coding to model imbalanced datasets.” 2018. Doctoral Dissertation, Georgia Tech. Accessed January 18, 2020. http://hdl.handle.net/1853/59919.

MLA Handbook (7^{th} Edition):

Whitaker, Bradley M. “Modifying sparse coding to model imbalanced datasets.” 2018. Web. 18 Jan 2020.

Vancouver:

Whitaker BM. Modifying sparse coding to model imbalanced datasets. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/1853/59919.

Council of Science Editors:

Whitaker BM. Modifying sparse coding to model imbalanced datasets. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59919

Stellenbosch University

7.
Stulumani, Agrippa.
Classification in high dimensional *data* using *sparse* techniques.

Degree: MCom, Statistics and Actuarial Science, 2019, Stellenbosch University

URL: http://hdl.handle.net/10019.1/105792

►

ENGLISH SUMMARY : Traditional classification techniques fail in the analysis of high-dimensional *data*. In response, new classification techniques and accompanying theory have recently emerged. These…
(more)

Subjects/Keywords: High dimensional data; Mathematical statistics; Sparse classification; Sparse grids; Dimension reduction (Statistics); UCTD

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

APA (6^{th} Edition):

Stulumani, A. (2019). Classification in high dimensional data using sparse techniques. (Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/105792

Not specified: Masters Thesis or Doctoral Dissertation

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

Stulumani, Agrippa. “Classification in high dimensional data using sparse techniques.” 2019. Thesis, Stellenbosch University. Accessed January 18, 2020. http://hdl.handle.net/10019.1/105792.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Stulumani, Agrippa. “Classification in high dimensional data using sparse techniques.” 2019. Web. 18 Jan 2020.

Vancouver:

Stulumani A. Classification in high dimensional data using sparse techniques. [Internet] [Thesis]. Stellenbosch University; 2019. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/10019.1/105792.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Stulumani A. Classification in high dimensional data using sparse techniques. [Thesis]. Stellenbosch University; 2019. Available from: http://hdl.handle.net/10019.1/105792

Not specified: Masters Thesis or Doctoral Dissertation

University of Lethbridge

8.
University of Lethbridge. Faculty of Arts and Science.
An improved implementation of sparsity detection of *sparse* derivative matrices
.

Degree: 2018, University of Lethbridge

URL: http://hdl.handle.net/10133/5266

► Optimization is a crucial branch of research with application in numerous domain. Determination of sparsity is a vital stream of optimization research with potentials for…
(more)

Subjects/Keywords: Jacobians; Combinatorial optimization; Sparse matrices – Data processing; Graph coloring; Parallel programs (Computer programs); Matix devrivatives; sparse data structure; CPR algorithm; sparse derivative matrices; Jacobian matrix; multilevel algorithm; parallel implementation

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

APA (6^{th} Edition):

Science, U. o. L. F. o. A. a. (2018). An improved implementation of sparsity detection of sparse derivative matrices . (Thesis). University of Lethbridge. Retrieved from http://hdl.handle.net/10133/5266

Not specified: Masters Thesis or Doctoral Dissertation

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

Science, University of Lethbridge. Faculty of Arts and. “An improved implementation of sparsity detection of sparse derivative matrices .” 2018. Thesis, University of Lethbridge. Accessed January 18, 2020. http://hdl.handle.net/10133/5266.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Science, University of Lethbridge. Faculty of Arts and. “An improved implementation of sparsity detection of sparse derivative matrices .” 2018. Web. 18 Jan 2020.

Vancouver:

Science UoLFoAa. An improved implementation of sparsity detection of sparse derivative matrices . [Internet] [Thesis]. University of Lethbridge; 2018. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/10133/5266.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Science UoLFoAa. An improved implementation of sparsity detection of sparse derivative matrices . [Thesis]. University of Lethbridge; 2018. Available from: http://hdl.handle.net/10133/5266

Not specified: Masters Thesis or Doctoral Dissertation

University of Minnesota

9.
Ebtehaj, Mohammad.
Leveraging sparsity in variational *data* assimilation.

Degree: MS, Mathematics, 2013, University of Minnesota

URL: http://hdl.handle.net/11299/162311

► Nowadays *data* assimilation is an essential component of any effective environmental prediction system. Environmental prediction models are, indeed, initial value problems and their forecast skills…
(more)

Subjects/Keywords: Data assimilation; Discrete cosine domian; Sparse regularization; Wavelet

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

APA (6^{th} Edition):

Ebtehaj, M. (2013). Leveraging sparsity in variational data assimilation. (Masters Thesis). University of Minnesota. Retrieved from http://hdl.handle.net/11299/162311

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

Ebtehaj, Mohammad. “Leveraging sparsity in variational data assimilation.” 2013. Masters Thesis, University of Minnesota. Accessed January 18, 2020. http://hdl.handle.net/11299/162311.

MLA Handbook (7^{th} Edition):

Ebtehaj, Mohammad. “Leveraging sparsity in variational data assimilation.” 2013. Web. 18 Jan 2020.

Vancouver:

Ebtehaj M. Leveraging sparsity in variational data assimilation. [Internet] [Masters thesis]. University of Minnesota; 2013. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/11299/162311.

Council of Science Editors:

Ebtehaj M. Leveraging sparsity in variational data assimilation. [Masters Thesis]. University of Minnesota; 2013. Available from: http://hdl.handle.net/11299/162311

University of Technology, Sydney

10. Zhou, Tianyi. Compressed learning.

Degree: 2013, University of Technology, Sydney

URL: http://hdl.handle.net/10453/24180

► There has been an explosion of *data* derived from the internet and other digital sources. These *data* are usually multi-dimensional, massive in volume, frequently incomplete,…
(more)

Subjects/Keywords: Compressed learning.; Sparse learning.; Machine learning.; Manifold learning.; Big data

Record Details Similar Records

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APA (6^{th} Edition):

Zhou, T. (2013). Compressed learning. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/24180

Not specified: Masters Thesis or Doctoral Dissertation

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

Zhou, Tianyi. “Compressed learning.” 2013. Thesis, University of Technology, Sydney. Accessed January 18, 2020. http://hdl.handle.net/10453/24180.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhou, Tianyi. “Compressed learning.” 2013. Web. 18 Jan 2020.

Vancouver:

Zhou T. Compressed learning. [Internet] [Thesis]. University of Technology, Sydney; 2013. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/10453/24180.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhou T. Compressed learning. [Thesis]. University of Technology, Sydney; 2013. Available from: http://hdl.handle.net/10453/24180

Not specified: Masters Thesis or Doctoral Dissertation

11.
Bonner, Ashley.
Contributions to *Sparse* Statistical Methods for *Data* Integration.

Degree: PhD, 2018, McMaster University

URL: http://hdl.handle.net/11375/24009

►

Background: Scientists are measuring multiple sources of massive, complex, and diverse *data* in hopes to better understand the principles underpinning complex phenomena. Sophisticated statistical and…
(more)

Subjects/Keywords: biostatistics; statistics; genetics; genomics; sparse methods; data integration

Record Details Similar Records

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

APA (6^{th} Edition):

Bonner, A. (2018). Contributions to Sparse Statistical Methods for Data Integration. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/24009

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

Bonner, Ashley. “Contributions to Sparse Statistical Methods for Data Integration.” 2018. Doctoral Dissertation, McMaster University. Accessed January 18, 2020. http://hdl.handle.net/11375/24009.

MLA Handbook (7^{th} Edition):

Bonner, Ashley. “Contributions to Sparse Statistical Methods for Data Integration.” 2018. Web. 18 Jan 2020.

Vancouver:

Bonner A. Contributions to Sparse Statistical Methods for Data Integration. [Internet] [Doctoral dissertation]. McMaster University; 2018. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/11375/24009.

Council of Science Editors:

Bonner A. Contributions to Sparse Statistical Methods for Data Integration. [Doctoral Dissertation]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/24009

Tulane University

12.
Gossmann, Alexej.
Regaining control of false findings in feature selection, classification, and prediction on neuroimaging and genomics * data*.

Degree: 2018, Tulane University

URL: https://digitallibrary.tulane.edu/islandora/object/tulane:80099

►

The technological advances of past decades have led to the accumulation of large amounts of genomic and neuroimaging *data*, enabling novel strategies in precision medicine.…
(more)

Subjects/Keywords: Sparse models; False discovery rate control; Data reuse

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APA (6^{th} Edition):

Gossmann, A. (2018). Regaining control of false findings in feature selection, classification, and prediction on neuroimaging and genomics data. (Thesis). Tulane University. Retrieved from https://digitallibrary.tulane.edu/islandora/object/tulane:80099

Not specified: Masters Thesis or Doctoral Dissertation

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

Gossmann, Alexej. “Regaining control of false findings in feature selection, classification, and prediction on neuroimaging and genomics data.” 2018. Thesis, Tulane University. Accessed January 18, 2020. https://digitallibrary.tulane.edu/islandora/object/tulane:80099.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Gossmann, Alexej. “Regaining control of false findings in feature selection, classification, and prediction on neuroimaging and genomics data.” 2018. Web. 18 Jan 2020.

Vancouver:

Gossmann A. Regaining control of false findings in feature selection, classification, and prediction on neuroimaging and genomics data. [Internet] [Thesis]. Tulane University; 2018. [cited 2020 Jan 18]. Available from: https://digitallibrary.tulane.edu/islandora/object/tulane:80099.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gossmann A. Regaining control of false findings in feature selection, classification, and prediction on neuroimaging and genomics data. [Thesis]. Tulane University; 2018. Available from: https://digitallibrary.tulane.edu/islandora/object/tulane:80099

Not specified: Masters Thesis or Doctoral Dissertation

University of Guelph

13.
Bak, Stephen.
Generalized linear regression model with LASSO, group LASSO, and *sparse* group LASSO regularization methods for finding bacteria associated with colorectal cancer using microbiome * data*
.

Degree: 2017, University of Guelph

URL: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/12096

► With ever increasing advancements in microbiome sequencing technologies, the need for efficient statistical modelling of these systems has become apparent. Most microbiome *data* is filled…
(more)

Subjects/Keywords: LASSO; regression; Microbiome; data; cancer; colon; regularization; multinomial; binomial; sparse; group

Record Details Similar Records

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

APA (6^{th} Edition):

Bak, S. (2017). Generalized linear regression model with LASSO, group LASSO, and sparse group LASSO regularization methods for finding bacteria associated with colorectal cancer using microbiome data . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/12096

Not specified: Masters Thesis or Doctoral Dissertation

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

Bak, Stephen. “Generalized linear regression model with LASSO, group LASSO, and sparse group LASSO regularization methods for finding bacteria associated with colorectal cancer using microbiome data .” 2017. Thesis, University of Guelph. Accessed January 18, 2020. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/12096.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bak, Stephen. “Generalized linear regression model with LASSO, group LASSO, and sparse group LASSO regularization methods for finding bacteria associated with colorectal cancer using microbiome data .” 2017. Web. 18 Jan 2020.

Vancouver:

Bak S. Generalized linear regression model with LASSO, group LASSO, and sparse group LASSO regularization methods for finding bacteria associated with colorectal cancer using microbiome data . [Internet] [Thesis]. University of Guelph; 2017. [cited 2020 Jan 18]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/12096.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bak S. Generalized linear regression model with LASSO, group LASSO, and sparse group LASSO regularization methods for finding bacteria associated with colorectal cancer using microbiome data . [Thesis]. University of Guelph; 2017. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/12096

Not specified: Masters Thesis or Doctoral Dissertation

Arizona State University

14.
Thulasiram, Ramesh L.
* Sparse* Learning Package with Stability Selection and
Application to Alzheimer's Disease.

Degree: MS, Computer Science, 2011, Arizona State University

URL: http://repository.asu.edu/items/9486

► *Sparse* learning is a technique in machine learning for feature selection and dimensionality reduction, to find a *sparse* set of the most relevant features. In…
(more)

Subjects/Keywords: Computer Science; Statistics; Mathematics; Data Mining; Lasso; Machine Learning; Sparse Learning

Record Details Similar Records

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

APA (6^{th} Edition):

Thulasiram, R. L. (2011). Sparse Learning Package with Stability Selection and Application to Alzheimer's Disease. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/9486

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

Thulasiram, Ramesh L. “Sparse Learning Package with Stability Selection and Application to Alzheimer's Disease.” 2011. Masters Thesis, Arizona State University. Accessed January 18, 2020. http://repository.asu.edu/items/9486.

MLA Handbook (7^{th} Edition):

Thulasiram, Ramesh L. “Sparse Learning Package with Stability Selection and Application to Alzheimer's Disease.” 2011. Web. 18 Jan 2020.

Vancouver:

Thulasiram RL. Sparse Learning Package with Stability Selection and Application to Alzheimer's Disease. [Internet] [Masters thesis]. Arizona State University; 2011. [cited 2020 Jan 18]. Available from: http://repository.asu.edu/items/9486.

Council of Science Editors:

Thulasiram RL. Sparse Learning Package with Stability Selection and Application to Alzheimer's Disease. [Masters Thesis]. Arizona State University; 2011. Available from: http://repository.asu.edu/items/9486

University of Texas – Austin

15. Kim, Youngchun. Signal acquisition challenges in mobile systems.

Degree: PhD, Electrical and Computer Engineering, 2018, University of Texas – Austin

URL: http://hdl.handle.net/2152/68089

► In recent decades, the advent of mobile computing has changed human lives by providing information that was not available in the past. The mobile computing…
(more)

Subjects/Keywords: Sparse signal processing; Compressed sensing; Random sampling; Data converter; Sequential detection

Record Details Similar Records

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

APA (6^{th} Edition):

Kim, Y. (2018). Signal acquisition challenges in mobile systems. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68089

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

Kim, Youngchun. “Signal acquisition challenges in mobile systems.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed January 18, 2020. http://hdl.handle.net/2152/68089.

MLA Handbook (7^{th} Edition):

Kim, Youngchun. “Signal acquisition challenges in mobile systems.” 2018. Web. 18 Jan 2020.

Vancouver:

Kim Y. Signal acquisition challenges in mobile systems. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/2152/68089.

Council of Science Editors:

Kim Y. Signal acquisition challenges in mobile systems. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68089

Princeton University

16.
Chung, Neo Christopher Honghoon.
Statistical Inference of Variables Driving Systematic Variation in High-Dimensional Biological * Data*
.

Degree: PhD, 2014, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01rv042w30x

► Modern genomic technologies collect an ever-increasing amount of information (e.g., gene expression and genotypes) about model organisms and humans. Systematic patterns of variation in such…
(more)

Subjects/Keywords: data; jackstraw; latent variable model; principal component analysis; resampling; sparse pca

Record Details Similar Records

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

APA (6^{th} Edition):

Chung, N. C. H. (2014). Statistical Inference of Variables Driving Systematic Variation in High-Dimensional Biological Data . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01rv042w30x

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

Chung, Neo Christopher Honghoon. “Statistical Inference of Variables Driving Systematic Variation in High-Dimensional Biological Data .” 2014. Doctoral Dissertation, Princeton University. Accessed January 18, 2020. http://arks.princeton.edu/ark:/88435/dsp01rv042w30x.

MLA Handbook (7^{th} Edition):

Chung, Neo Christopher Honghoon. “Statistical Inference of Variables Driving Systematic Variation in High-Dimensional Biological Data .” 2014. Web. 18 Jan 2020.

Vancouver:

Chung NCH. Statistical Inference of Variables Driving Systematic Variation in High-Dimensional Biological Data . [Internet] [Doctoral dissertation]. Princeton University; 2014. [cited 2020 Jan 18]. Available from: http://arks.princeton.edu/ark:/88435/dsp01rv042w30x.

Council of Science Editors:

Chung NCH. Statistical Inference of Variables Driving Systematic Variation in High-Dimensional Biological Data . [Doctoral Dissertation]. Princeton University; 2014. Available from: http://arks.princeton.edu/ark:/88435/dsp01rv042w30x

17.
Tran, Loc.
High Dimensional *Data* Set Analysis Using a Large-Scale Manifold Learning Approach.

Degree: PhD, Electrical/Computer Engineering, 2014, Old Dominion University

URL: 9781321316513 ; https://digitalcommons.odu.edu/ece_etds/186

► Because of technological advances, a trend occurs for *data* sets increasing in size and dimensionality. Processing these large scale *data* sets is challenging for…
(more)

Subjects/Keywords: Manifold learning; Sparse learning; Manifolds; Big data; Computer Engineering; Computer Sciences

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

APA (6^{th} Edition):

Tran, L. (2014). High Dimensional Data Set Analysis Using a Large-Scale Manifold Learning Approach. (Doctoral Dissertation). Old Dominion University. Retrieved from 9781321316513 ; https://digitalcommons.odu.edu/ece_etds/186

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

Tran, Loc. “High Dimensional Data Set Analysis Using a Large-Scale Manifold Learning Approach.” 2014. Doctoral Dissertation, Old Dominion University. Accessed January 18, 2020. 9781321316513 ; https://digitalcommons.odu.edu/ece_etds/186.

MLA Handbook (7^{th} Edition):

Tran, Loc. “High Dimensional Data Set Analysis Using a Large-Scale Manifold Learning Approach.” 2014. Web. 18 Jan 2020.

Vancouver:

Tran L. High Dimensional Data Set Analysis Using a Large-Scale Manifold Learning Approach. [Internet] [Doctoral dissertation]. Old Dominion University; 2014. [cited 2020 Jan 18]. Available from: 9781321316513 ; https://digitalcommons.odu.edu/ece_etds/186.

Council of Science Editors:

Tran L. High Dimensional Data Set Analysis Using a Large-Scale Manifold Learning Approach. [Doctoral Dissertation]. Old Dominion University; 2014. Available from: 9781321316513 ; https://digitalcommons.odu.edu/ece_etds/186

Halmstad University

18.
Vogetseder, Georg.
Functional Analysis of Real World Truck Fuel Consumption * Data*.

Degree: Computer and Electrical Engineering (IDE), 2008, Halmstad University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1148

► This thesis covers the analysis of *sparse* and irregular fuel consumption *data* of long distance haulage articulate trucks. It is shown that this kind…
(more)

Subjects/Keywords: PCA; Clustering; Sparse data

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APA (6^{th} Edition):

Vogetseder, G. (2008). Functional Analysis of Real World Truck Fuel Consumption Data. (Thesis). Halmstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1148

Not specified: Masters Thesis or Doctoral Dissertation

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

Vogetseder, Georg. “Functional Analysis of Real World Truck Fuel Consumption Data.” 2008. Thesis, Halmstad University. Accessed January 18, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1148.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Vogetseder, Georg. “Functional Analysis of Real World Truck Fuel Consumption Data.” 2008. Web. 18 Jan 2020.

Vancouver:

Vogetseder G. Functional Analysis of Real World Truck Fuel Consumption Data. [Internet] [Thesis]. Halmstad University; 2008. [cited 2020 Jan 18]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1148.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vogetseder G. Functional Analysis of Real World Truck Fuel Consumption Data. [Thesis]. Halmstad University; 2008. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1148

Not specified: Masters Thesis or Doctoral Dissertation

Georgia State University

19.
Wu, Xiaolong.
Optimizing *Sparse* Matrix-Matrix Multiplication on a Heterogeneous CPU-GPU Platform.

Degree: MS, Computer Science, 2015, Georgia State University

URL: https://scholarworks.gsu.edu/cs_theses/84

► *Sparse* Matrix-Matrix multiplication (SpMM) is a fundamental operation over irregular *data*, which is widely used in graph algorithms, such as finding minimum spanning trees…
(more)

Subjects/Keywords: Sparse matrix-matrix multiplication; Data locality; Pipelining; GPU

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

APA (6^{th} Edition):

Wu, X. (2015). Optimizing Sparse Matrix-Matrix Multiplication on a Heterogeneous CPU-GPU Platform. (Thesis). Georgia State University. Retrieved from https://scholarworks.gsu.edu/cs_theses/84

Not specified: Masters Thesis or Doctoral Dissertation

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

Wu, Xiaolong. “Optimizing Sparse Matrix-Matrix Multiplication on a Heterogeneous CPU-GPU Platform.” 2015. Thesis, Georgia State University. Accessed January 18, 2020. https://scholarworks.gsu.edu/cs_theses/84.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Wu, Xiaolong. “Optimizing Sparse Matrix-Matrix Multiplication on a Heterogeneous CPU-GPU Platform.” 2015. Web. 18 Jan 2020.

Vancouver:

Wu X. Optimizing Sparse Matrix-Matrix Multiplication on a Heterogeneous CPU-GPU Platform. [Internet] [Thesis]. Georgia State University; 2015. [cited 2020 Jan 18]. Available from: https://scholarworks.gsu.edu/cs_theses/84.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wu X. Optimizing Sparse Matrix-Matrix Multiplication on a Heterogeneous CPU-GPU Platform. [Thesis]. Georgia State University; 2015. Available from: https://scholarworks.gsu.edu/cs_theses/84

Not specified: Masters Thesis or Doctoral Dissertation

Iowa State University

20.
Zhu, Weicheng.
Topics in *sparse* functional *data* analysis.

Degree: 2018, Iowa State University

URL: https://lib.dr.iastate.edu/etd/17378

► This dissertation consists of three research papers that address different problems in modeling *sparse* functional *data*. The first paper (Chapter 2) focuses on the statistical…
(more)

Subjects/Keywords: HMRI; image imputation; R; sparse functional data; STFIT; Statistics and Probability

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

APA (6^{th} Edition):

Zhu, W. (2018). Topics in sparse functional data analysis. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/17378

Not specified: Masters Thesis or Doctoral Dissertation

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

Zhu, Weicheng. “Topics in sparse functional data analysis.” 2018. Thesis, Iowa State University. Accessed January 18, 2020. https://lib.dr.iastate.edu/etd/17378.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhu, Weicheng. “Topics in sparse functional data analysis.” 2018. Web. 18 Jan 2020.

Vancouver:

Zhu W. Topics in sparse functional data analysis. [Internet] [Thesis]. Iowa State University; 2018. [cited 2020 Jan 18]. Available from: https://lib.dr.iastate.edu/etd/17378.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhu W. Topics in sparse functional data analysis. [Thesis]. Iowa State University; 2018. Available from: https://lib.dr.iastate.edu/etd/17378

Not specified: Masters Thesis or Doctoral Dissertation

21.
Gullipalli, Deep Kumar.
* Data*
envelopment analysis with

Degree: MS, Department of Industrial & Manufacturing Systems Engineering, 2011, Kansas State University

URL: http://hdl.handle.net/2097/13092

► Quest for continuous improvement among the organizations and issue of missing *data* for *data* analysis are never ending. This thesis brings these two topics under…
(more)

Subjects/Keywords: Data envelopment analysis; Sparse data; Missing values; Healthcare; Clustering; Fuzzy Set Theory; Industrial Engineering (0546)

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APA (6^{th} Edition):

Gullipalli, D. K. (2011). Data envelopment analysis with sparse data. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/13092

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

Gullipalli, Deep Kumar. “Data envelopment analysis with sparse data.” 2011. Masters Thesis, Kansas State University. Accessed January 18, 2020. http://hdl.handle.net/2097/13092.

MLA Handbook (7^{th} Edition):

Gullipalli, Deep Kumar. “Data envelopment analysis with sparse data.” 2011. Web. 18 Jan 2020.

Vancouver:

Gullipalli DK. Data envelopment analysis with sparse data. [Internet] [Masters thesis]. Kansas State University; 2011. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/2097/13092.

Council of Science Editors:

Gullipalli DK. Data envelopment analysis with sparse data. [Masters Thesis]. Kansas State University; 2011. Available from: http://hdl.handle.net/2097/13092

University of Lethbridge

22.
University of Lethbridge. Faculty of Arts and Science.
A Computational study of *sparse* or structured matrix operations
.

Degree: 2018, University of Lethbridge

URL: http://hdl.handle.net/10133/5268

► Matrix computation is an important area in high-performance scientific computing. Major computer manufacturers and vendors typically provide architecture- aware implementation libraries such as Basic Linear…
(more)

Subjects/Keywords: Sparse matrices – Data processing; Java (Computer program language); Algebras, linear; High performance computing; Mathematical optimization – Data processing; Numerical calculations – Data processing; sparse data structure; CRS; Compressed Row Storage; JSA; Java Sparse Array; diagonal; BLAS; Basic Linear Algebra Subroutines

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

APA (6^{th} Edition):

Science, U. o. L. F. o. A. a. (2018). A Computational study of sparse or structured matrix operations . (Thesis). University of Lethbridge. Retrieved from http://hdl.handle.net/10133/5268

Not specified: Masters Thesis or Doctoral Dissertation

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

Science, University of Lethbridge. Faculty of Arts and. “A Computational study of sparse or structured matrix operations .” 2018. Thesis, University of Lethbridge. Accessed January 18, 2020. http://hdl.handle.net/10133/5268.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Science, University of Lethbridge. Faculty of Arts and. “A Computational study of sparse or structured matrix operations .” 2018. Web. 18 Jan 2020.

Vancouver:

Science UoLFoAa. A Computational study of sparse or structured matrix operations . [Internet] [Thesis]. University of Lethbridge; 2018. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/10133/5268.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Science UoLFoAa. A Computational study of sparse or structured matrix operations . [Thesis]. University of Lethbridge; 2018. Available from: http://hdl.handle.net/10133/5268

Not specified: Masters Thesis or Doctoral Dissertation

University of South Florida

23.
Quintero, Michael C.
Constructing a Clinical Research *Data* Management System.

Degree: 2017, University of South Florida

URL: https://scholarcommons.usf.edu/etd/7081

► Clinical study *data* is usually collected without knowing what kind of *data* is going to be collected in advance. In addition, all of the possible…
(more)

Subjects/Keywords: Sparse Data Storage; Entity Attribute Value Data Model; Database Modeling; Wide Tables; Clinical Study Data; Computer Sciences; Databases and Information Systems

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APA (6^{th} Edition):

Quintero, M. C. (2017). Constructing a Clinical Research Data Management System. (Thesis). University of South Florida. Retrieved from https://scholarcommons.usf.edu/etd/7081

Not specified: Masters Thesis or Doctoral Dissertation

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

Quintero, Michael C. “Constructing a Clinical Research Data Management System.” 2017. Thesis, University of South Florida. Accessed January 18, 2020. https://scholarcommons.usf.edu/etd/7081.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Quintero, Michael C. “Constructing a Clinical Research Data Management System.” 2017. Web. 18 Jan 2020.

Vancouver:

Quintero MC. Constructing a Clinical Research Data Management System. [Internet] [Thesis]. University of South Florida; 2017. [cited 2020 Jan 18]. Available from: https://scholarcommons.usf.edu/etd/7081.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Quintero MC. Constructing a Clinical Research Data Management System. [Thesis]. University of South Florida; 2017. Available from: https://scholarcommons.usf.edu/etd/7081

Not specified: Masters Thesis or Doctoral Dissertation

University of Minnesota

24.
Yi, Feng.
Selected topics of high-dimensional *sparse* modeling.

Degree: PhD, Statistics, 2013, University of Minnesota

URL: http://hdl.handle.net/11299/161965

► In this thesis we study three problems over high-dimensional *sparse* modeling. We first discuss the problem of high-dimensional covariance matrix estimation. Nowadays, massive high-dimensional *data*…
(more)

Subjects/Keywords: Covariance matrix; Factor analysis; High-dimensional data analysis; Non-parametric method; Sparse modeling

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

APA (6^{th} Edition):

Yi, F. (2013). Selected topics of high-dimensional sparse modeling. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/161965

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

Yi, Feng. “Selected topics of high-dimensional sparse modeling.” 2013. Doctoral Dissertation, University of Minnesota. Accessed January 18, 2020. http://hdl.handle.net/11299/161965.

MLA Handbook (7^{th} Edition):

Yi, Feng. “Selected topics of high-dimensional sparse modeling.” 2013. Web. 18 Jan 2020.

Vancouver:

Yi F. Selected topics of high-dimensional sparse modeling. [Internet] [Doctoral dissertation]. University of Minnesota; 2013. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/11299/161965.

Council of Science Editors:

Yi F. Selected topics of high-dimensional sparse modeling. [Doctoral Dissertation]. University of Minnesota; 2013. Available from: http://hdl.handle.net/11299/161965

Rice University

25.
Yang, Yongchao.
Harnessing *data* structure for health monitoring and assessment of civil structures: *sparse* representation and low-rank structure.

Degree: PhD, Engineering, 2014, Rice University

URL: http://hdl.handle.net/1911/87779

► Civil structures are subjected to ambient loads, natural hazards, and man-made extreme events, which can cause deterioration, damage, and even catastrophic failure of structures. Dense…
(more)

Subjects/Keywords: Structural health monitoring; system identification; damage detection; data-driven methods; sparse representation; blind source separation

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

APA (6^{th} Edition):

Yang, Y. (2014). Harnessing data structure for health monitoring and assessment of civil structures: sparse representation and low-rank structure. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/87779

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

Yang, Yongchao. “Harnessing data structure for health monitoring and assessment of civil structures: sparse representation and low-rank structure.” 2014. Doctoral Dissertation, Rice University. Accessed January 18, 2020. http://hdl.handle.net/1911/87779.

MLA Handbook (7^{th} Edition):

Yang, Yongchao. “Harnessing data structure for health monitoring and assessment of civil structures: sparse representation and low-rank structure.” 2014. Web. 18 Jan 2020.

Vancouver:

Yang Y. Harnessing data structure for health monitoring and assessment of civil structures: sparse representation and low-rank structure. [Internet] [Doctoral dissertation]. Rice University; 2014. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/1911/87779.

Council of Science Editors:

Yang Y. Harnessing data structure for health monitoring and assessment of civil structures: sparse representation and low-rank structure. [Doctoral Dissertation]. Rice University; 2014. Available from: http://hdl.handle.net/1911/87779

University of Rochester

26.
Song, Yanwei.
Energy efficient *data* movement with *sparse*
representation.

Degree: PhD, 2016, University of Rochester

URL: http://hdl.handle.net/1802/30637

► Energy efficiency is one of the most significant requirements in the study of computer systems, from mobile devices to large-scale *data* centers. *Data* movement is…
(more)

Subjects/Keywords: Data movement; Energy efficient; Memory interface; On-chip interconnect; Opportunistic coding; Sparse representation

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

APA (6^{th} Edition):

Song, Y. (2016). Energy efficient data movement with sparse representation. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/30637

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

Song, Yanwei. “Energy efficient data movement with sparse representation.” 2016. Doctoral Dissertation, University of Rochester. Accessed January 18, 2020. http://hdl.handle.net/1802/30637.

MLA Handbook (7^{th} Edition):

Song, Yanwei. “Energy efficient data movement with sparse representation.” 2016. Web. 18 Jan 2020.

Vancouver:

Song Y. Energy efficient data movement with sparse representation. [Internet] [Doctoral dissertation]. University of Rochester; 2016. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/1802/30637.

Council of Science Editors:

Song Y. Energy efficient data movement with sparse representation. [Doctoral Dissertation]. University of Rochester; 2016. Available from: http://hdl.handle.net/1802/30637

Case Western Reserve University

27.
ChangHyun, Lee.
PSG *Data* Compression And Decompression Based On Compressed
Sensing.

Degree: MSs (Engineering), EECS - System and Control Engineering, 2011, Case Western Reserve University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=case1310065394

► In the thesis, the compression and decompression scheme based on Compressive Sensing (CS) is developed for multichannel polysomnography(PSG) *data*. This thesis is composed of three…
(more)

Subjects/Keywords: Electrical Engineering; Health Care; PSG; Sparse Representation; Compressed Sensing; Data Compression; Convex Optimization

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

APA (6^{th} Edition):

ChangHyun, L. (2011). PSG Data Compression And Decompression Based On Compressed Sensing. (Masters Thesis). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1310065394

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

ChangHyun, Lee. “PSG Data Compression And Decompression Based On Compressed Sensing.” 2011. Masters Thesis, Case Western Reserve University. Accessed January 18, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1310065394.

MLA Handbook (7^{th} Edition):

ChangHyun, Lee. “PSG Data Compression And Decompression Based On Compressed Sensing.” 2011. Web. 18 Jan 2020.

Vancouver:

ChangHyun L. PSG Data Compression And Decompression Based On Compressed Sensing. [Internet] [Masters thesis]. Case Western Reserve University; 2011. [cited 2020 Jan 18]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1310065394.

Council of Science Editors:

ChangHyun L. PSG Data Compression And Decompression Based On Compressed Sensing. [Masters Thesis]. Case Western Reserve University; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1310065394

28.
Purdy, David Gregory.
* Sparse* Models for

Degree: Statistics, 2012, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/9qb472v2

► Significant recent advances in many areas of *data* collection and processing have introduced many challenges for modeling such *data*. *Data* sets have exploded in the…
(more)

Subjects/Keywords: Statistics; Computer science; Machine Learning; Model Diagnostics; Recommendation Systems; Sparse Data; Statistics; Visualization

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

APA (6^{th} Edition):

Purdy, D. G. (2012). Sparse Models for Sparse Data: Methods, Limitations, Visualizations, and Ensembles. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/9qb472v2

Not specified: Masters Thesis or Doctoral Dissertation

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

Purdy, David Gregory. “Sparse Models for Sparse Data: Methods, Limitations, Visualizations, and Ensembles.” 2012. Thesis, University of California – Berkeley. Accessed January 18, 2020. http://www.escholarship.org/uc/item/9qb472v2.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Purdy, David Gregory. “Sparse Models for Sparse Data: Methods, Limitations, Visualizations, and Ensembles.” 2012. Web. 18 Jan 2020.

Vancouver:

Purdy DG. Sparse Models for Sparse Data: Methods, Limitations, Visualizations, and Ensembles. [Internet] [Thesis]. University of California – Berkeley; 2012. [cited 2020 Jan 18]. Available from: http://www.escholarship.org/uc/item/9qb472v2.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Purdy DG. Sparse Models for Sparse Data: Methods, Limitations, Visualizations, and Ensembles. [Thesis]. University of California – Berkeley; 2012. Available from: http://www.escholarship.org/uc/item/9qb472v2

Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech

29. Cain, Christopher Hawthorn. Real Time SLAM Using Compressed Occupancy Grids For a Low Cost Autonomous Underwater Vehicle.

Degree: PhD, Mechanical Engineering, 2014, Virginia Tech

URL: http://hdl.handle.net/10919/47920

► The research presented in this dissertation pertains to the development of a real time SLAM solution that can be performed by a low cost autonomous…
(more)

Subjects/Keywords: Autonomous Vehicles; SLAM; Occupancy Grids; Haar Wavelet Transform; Compressed Sensing; Sparse Signal Reconstruction; Data Compression

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APA (6^{th} Edition):

Cain, C. H. (2014). Real Time SLAM Using Compressed Occupancy Grids For a Low Cost Autonomous Underwater Vehicle. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/47920

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

Cain, Christopher Hawthorn. “Real Time SLAM Using Compressed Occupancy Grids For a Low Cost Autonomous Underwater Vehicle.” 2014. Doctoral Dissertation, Virginia Tech. Accessed January 18, 2020. http://hdl.handle.net/10919/47920.

MLA Handbook (7^{th} Edition):

Cain, Christopher Hawthorn. “Real Time SLAM Using Compressed Occupancy Grids For a Low Cost Autonomous Underwater Vehicle.” 2014. Web. 18 Jan 2020.

Vancouver:

Cain CH. Real Time SLAM Using Compressed Occupancy Grids For a Low Cost Autonomous Underwater Vehicle. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/10919/47920.

Council of Science Editors:

Cain CH. Real Time SLAM Using Compressed Occupancy Grids For a Low Cost Autonomous Underwater Vehicle. [Doctoral Dissertation]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/47920

30. Seetharaman, Indu. Consistent bi-level variable selection via composite group bridge penalized regression.

Degree: MS, Department of Statistics, 2013, Kansas State University

URL: http://hdl.handle.net/2097/15980

► We study the composite group bridge penalized regression methods for conducting bilevel variable selection in high dimensional linear regression models with a diverging number of…
(more)

Subjects/Keywords: Bi-level variable selection; High-dimensional data; Oracle property; Penalized regression; Sparse models; Statistics (0463)

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

APA (6^{th} Edition):

Seetharaman, I. (2013). Consistent bi-level variable selection via composite group bridge penalized regression. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/15980

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

Seetharaman, Indu. “Consistent bi-level variable selection via composite group bridge penalized regression.” 2013. Masters Thesis, Kansas State University. Accessed January 18, 2020. http://hdl.handle.net/2097/15980.

MLA Handbook (7^{th} Edition):

Seetharaman, Indu. “Consistent bi-level variable selection via composite group bridge penalized regression.” 2013. Web. 18 Jan 2020.

Vancouver:

Seetharaman I. Consistent bi-level variable selection via composite group bridge penalized regression. [Internet] [Masters thesis]. Kansas State University; 2013. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/2097/15980.

Council of Science Editors:

Seetharaman I. Consistent bi-level variable selection via composite group bridge penalized regression. [Masters Thesis]. Kansas State University; 2013. Available from: http://hdl.handle.net/2097/15980