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277 total matches.

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Dates

- 2015 – 2019 (138)
- 2010 – 2014 (133)
- 2005 – 2009 (19)

Universities

- National University of Singapore (12)
- Paris Saclay (11)
- EPFL (10)

Department

- Statistics (16)
- Electrical Engineering (11)
- Electrical and Computer Engineering (11)

Degrees

- PhD (80)
- Docteur es (59)
- MS (13)

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University of Ontario Institute of Technology

1. Hamidi, Shahrokh. Sparse signal representation based algorithms with application to ultrasonic array imaging.

Degree: 2016, University of Ontario Institute of Technology

URL: http://hdl.handle.net/10155/672

► We address one- and two-layer ultrasonic array imaging. We use an array of transducers to inspect the internal structure of a given specimen. In the…
(more)

Subjects/Keywords: Sparsity; Ultrasonic array imaging

Record Details Similar Records

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

APA (6^{th} Edition):

Hamidi, S. (2016). Sparse signal representation based algorithms with application to ultrasonic array imaging. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/672

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):

Hamidi, Shahrokh. “Sparse signal representation based algorithms with application to ultrasonic array imaging.” 2016. Thesis, University of Ontario Institute of Technology. Accessed June 24, 2019. http://hdl.handle.net/10155/672.

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Hamidi, Shahrokh. “Sparse signal representation based algorithms with application to ultrasonic array imaging.” 2016. Web. 24 Jun 2019.

Vancouver:

Hamidi S. Sparse signal representation based algorithms with application to ultrasonic array imaging. [Internet] [Thesis]. University of Ontario Institute of Technology; 2016. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/10155/672.

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

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hamidi S. Sparse signal representation based algorithms with application to ultrasonic array imaging. [Thesis]. University of Ontario Institute of Technology; 2016. Available from: http://hdl.handle.net/10155/672

Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

2. Apaydin, Meltem. Phase Retrieval of Sparse Signals from Magnitude Information.

Degree: 2014, Texas A&M University

URL: http://hdl.handle.net/1969.1/153388

► The ability to recover the phase information of a signal of interest from a measurement process plays an important role in many practical applications. When…
(more)

Subjects/Keywords: phase retrieval; compressive sensing; sparsity

Record Details Similar Records

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APA (6^{th} Edition):

Apaydin, M. (2014). Phase Retrieval of Sparse Signals from Magnitude Information. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153388

Not specified: Masters Thesis or Doctoral Dissertation

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

Apaydin, Meltem. “Phase Retrieval of Sparse Signals from Magnitude Information.” 2014. Thesis, Texas A&M University. Accessed June 24, 2019. http://hdl.handle.net/1969.1/153388.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Apaydin, Meltem. “Phase Retrieval of Sparse Signals from Magnitude Information.” 2014. Web. 24 Jun 2019.

Vancouver:

Apaydin M. Phase Retrieval of Sparse Signals from Magnitude Information. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/1969.1/153388.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Apaydin M. Phase Retrieval of Sparse Signals from Magnitude Information. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153388

Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

3. Tutuk, Fatih. Sparse Linear Time Invariant System Identification Using Weighted Lasso.

Degree: MS, Electrical Engineering, 2014, Penn State University

URL: https://etda.libraries.psu.edu/catalog/23710

► In this thesis, the identification of a single-input single-output (SISO) linear time invariant (LTI) dynamical system from a finite set of completely known input signals…
(more)

Subjects/Keywords: System Identification; Sparsity; Lasso; Reweighting

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APA (6^{th} Edition):

Tutuk, F. (2014). Sparse Linear Time Invariant System Identification Using Weighted Lasso. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/23710

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

Tutuk, Fatih. “Sparse Linear Time Invariant System Identification Using Weighted Lasso.” 2014. Masters Thesis, Penn State University. Accessed June 24, 2019. https://etda.libraries.psu.edu/catalog/23710.

MLA Handbook (7^{th} Edition):

Tutuk, Fatih. “Sparse Linear Time Invariant System Identification Using Weighted Lasso.” 2014. Web. 24 Jun 2019.

Vancouver:

Tutuk F. Sparse Linear Time Invariant System Identification Using Weighted Lasso. [Internet] [Masters thesis]. Penn State University; 2014. [cited 2019 Jun 24]. Available from: https://etda.libraries.psu.edu/catalog/23710.

Council of Science Editors:

Tutuk F. Sparse Linear Time Invariant System Identification Using Weighted Lasso. [Masters Thesis]. Penn State University; 2014. Available from: https://etda.libraries.psu.edu/catalog/23710

UCLA

4.
Flynn, John Joseph.
Learning a simplicial structure using * sparsity*.

Degree: Statistics, 2014, UCLA

URL: http://www.escholarship.org/uc/item/52v7g1sp

► We discuss an application of *sparsity* to manifold learning. We show that the activation patterns of an over-complete basis can be used to build a…
(more)

Subjects/Keywords: Statistics; manifold learning; simplices; sparsity

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APA (6^{th} Edition):

Flynn, J. J. (2014). Learning a simplicial structure using sparsity. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/52v7g1sp

Not specified: Masters Thesis or Doctoral Dissertation

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

Flynn, John Joseph. “Learning a simplicial structure using sparsity.” 2014. Thesis, UCLA. Accessed June 24, 2019. http://www.escholarship.org/uc/item/52v7g1sp.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Flynn, John Joseph. “Learning a simplicial structure using sparsity.” 2014. Web. 24 Jun 2019.

Vancouver:

Flynn JJ. Learning a simplicial structure using sparsity. [Internet] [Thesis]. UCLA; 2014. [cited 2019 Jun 24]. Available from: http://www.escholarship.org/uc/item/52v7g1sp.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Flynn JJ. Learning a simplicial structure using sparsity. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/52v7g1sp

Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

5. Phillipson, Kaitlyn Rose. Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems.

Degree: PhD, Mathematics, 2016, Texas A&M University

URL: http://hdl.handle.net/1969.1/157880

► Computational algebraic geometry is the study of roots of polynomials and polynomial systems. We are familiar with the notion of degree, but there are other…
(more)

Subjects/Keywords: sparsity; sums of squares; approximations

Record Details Similar Records

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APA (6^{th} Edition):

Phillipson, K. R. (2016). Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/157880

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

Phillipson, Kaitlyn Rose. “Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems.” 2016. Doctoral Dissertation, Texas A&M University. Accessed June 24, 2019. http://hdl.handle.net/1969.1/157880.

MLA Handbook (7^{th} Edition):

Phillipson, Kaitlyn Rose. “Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems.” 2016. Web. 24 Jun 2019.

Vancouver:

Phillipson KR. Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems. [Internet] [Doctoral dissertation]. Texas A&M University; 2016. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/1969.1/157880.

Council of Science Editors:

Phillipson KR. Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems. [Doctoral Dissertation]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/157880

University of New Mexico

6.
Potluru, Vamsi.
Matrix Factorization: Nonnegativity, *Sparsity* and Independence.

Degree: Department of Computer Science, 2014, University of New Mexico

URL: http://hdl.handle.net/1928/24336

► Matrix factorization arises in a wide range of application domains and is useful for extracting the latent features in the dataset. Examples include recommender systems,…
(more)

Subjects/Keywords: Matrix factorization; Nonnegativity; Sparsity; Independence

Record Details Similar Records

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APA (6^{th} Edition):

Potluru, V. (2014). Matrix Factorization: Nonnegativity, Sparsity and Independence. (Doctoral Dissertation). University of New Mexico. Retrieved from http://hdl.handle.net/1928/24336

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

Potluru, Vamsi. “Matrix Factorization: Nonnegativity, Sparsity and Independence.” 2014. Doctoral Dissertation, University of New Mexico. Accessed June 24, 2019. http://hdl.handle.net/1928/24336.

MLA Handbook (7^{th} Edition):

Potluru, Vamsi. “Matrix Factorization: Nonnegativity, Sparsity and Independence.” 2014. Web. 24 Jun 2019.

Vancouver:

Potluru V. Matrix Factorization: Nonnegativity, Sparsity and Independence. [Internet] [Doctoral dissertation]. University of New Mexico; 2014. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/1928/24336.

Council of Science Editors:

Potluru V. Matrix Factorization: Nonnegativity, Sparsity and Independence. [Doctoral Dissertation]. University of New Mexico; 2014. Available from: http://hdl.handle.net/1928/24336

University of Alberta

7.
Bonar, Christopher David.
* Sparsity* and Group

Degree: MS, Department of Physics, 2012, University of Alberta

URL: https://era.library.ualberta.ca/files/ff365590j

► Local time-frequency analysis, also known as spectral decomposition, allows for a more detailed interpretation of time-series by providing the evolution of the frequency spectrum through…
(more)

Subjects/Keywords: Group Sparsity; Inversion; Seismic; Local Time-Frequency Analysis; Sparsity; Spectral Decomposition

Record Details Similar Records

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APA (6^{th} Edition):

Bonar, C. D. (2012). Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/ff365590j

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

Bonar, Christopher David. “Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data.” 2012. Masters Thesis, University of Alberta. Accessed June 24, 2019. https://era.library.ualberta.ca/files/ff365590j.

MLA Handbook (7^{th} Edition):

Bonar, Christopher David. “Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data.” 2012. Web. 24 Jun 2019.

Vancouver:

Bonar CD. Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data. [Internet] [Masters thesis]. University of Alberta; 2012. [cited 2019 Jun 24]. Available from: https://era.library.ualberta.ca/files/ff365590j.

Council of Science Editors:

Bonar CD. Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data. [Masters Thesis]. University of Alberta; 2012. Available from: https://era.library.ualberta.ca/files/ff365590j

University of Canterbury

8.
Wu, Bing.
Exploiting data *sparsity* in parallel magnetic resonance imaging.

Degree: Electrical and Computer Engineering, 2010, University of Canterbury

URL: http://hdl.handle.net/10092/3914

► Magnetic resonance imaging (MRI) is a widely employed imaging modality that allows observation of the interior of human body. Compared to other imaging modalities such…
(more)

Subjects/Keywords: Magnetic resonance imaging; image sparsity; parallel MRI

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APA (6^{th} Edition):

Wu, B. (2010). Exploiting data sparsity in parallel magnetic resonance imaging. (Thesis). University of Canterbury. Retrieved from http://hdl.handle.net/10092/3914

Not specified: Masters Thesis or Doctoral Dissertation

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

Wu, Bing. “Exploiting data sparsity in parallel magnetic resonance imaging.” 2010. Thesis, University of Canterbury. Accessed June 24, 2019. http://hdl.handle.net/10092/3914.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Wu, Bing. “Exploiting data sparsity in parallel magnetic resonance imaging.” 2010. Web. 24 Jun 2019.

Vancouver:

Wu B. Exploiting data sparsity in parallel magnetic resonance imaging. [Internet] [Thesis]. University of Canterbury; 2010. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/10092/3914.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wu B. Exploiting data sparsity in parallel magnetic resonance imaging. [Thesis]. University of Canterbury; 2010. Available from: http://hdl.handle.net/10092/3914

Not specified: Masters Thesis or Doctoral Dissertation

University of Washington

9. Tank, Alex. Discovering Interactions in Multivariate Time Series.

Degree: PhD, 2018, University of Washington

URL: http://hdl.handle.net/1773/43162

► In large collections of multivariate time series it is of interest to determine interactions between each pair of time series. Classically, interactions between time series…
(more)

Subjects/Keywords: Granger causality; sparsity; time series; Statistics; Statistics

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APA (6^{th} Edition):

Tank, A. (2018). Discovering Interactions in Multivariate Time Series. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/43162

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

Tank, Alex. “Discovering Interactions in Multivariate Time Series.” 2018. Doctoral Dissertation, University of Washington. Accessed June 24, 2019. http://hdl.handle.net/1773/43162.

MLA Handbook (7^{th} Edition):

Tank, Alex. “Discovering Interactions in Multivariate Time Series.” 2018. Web. 24 Jun 2019.

Vancouver:

Tank A. Discovering Interactions in Multivariate Time Series. [Internet] [Doctoral dissertation]. University of Washington; 2018. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/1773/43162.

Council of Science Editors:

Tank A. Discovering Interactions in Multivariate Time Series. [Doctoral Dissertation]. University of Washington; 2018. Available from: http://hdl.handle.net/1773/43162

Clemson University

10.
Cooper, John.
* Sparsity* Regularization in Diffuse Optical Tomography.

Degree: PhD, Mathematical Science, 2012, Clemson University

URL: https://tigerprints.clemson.edu/all_dissertations/1000

► The purpose of this dissertation is to improve image reconstruction in Diffuse Optical Tomography (DOT), a high contrast imaging modality that uses a near infrared…
(more)

Subjects/Keywords: Optical; Regularization; Sparsity; Tomography; Applied Mathematics

Record Details Similar Records

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APA (6^{th} Edition):

Cooper, J. (2012). Sparsity Regularization in Diffuse Optical Tomography. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/1000

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

Cooper, John. “Sparsity Regularization in Diffuse Optical Tomography.” 2012. Doctoral Dissertation, Clemson University. Accessed June 24, 2019. https://tigerprints.clemson.edu/all_dissertations/1000.

MLA Handbook (7^{th} Edition):

Cooper, John. “Sparsity Regularization in Diffuse Optical Tomography.” 2012. Web. 24 Jun 2019.

Vancouver:

Cooper J. Sparsity Regularization in Diffuse Optical Tomography. [Internet] [Doctoral dissertation]. Clemson University; 2012. [cited 2019 Jun 24]. Available from: https://tigerprints.clemson.edu/all_dissertations/1000.

Council of Science Editors:

Cooper J. Sparsity Regularization in Diffuse Optical Tomography. [Doctoral Dissertation]. Clemson University; 2012. Available from: https://tigerprints.clemson.edu/all_dissertations/1000

Université Catholique de Louvain

11.
Plumat, Jérôme.
Image classification and reconstruction using Markov Random Field modeling and * sparsity*.

Degree: 2012, Université Catholique de Louvain

URL: http://hdl.handle.net/2078.1/120111

►

The medical imaging requires fast and accurate volume reconstruction. This may be used to evaluate, before any treatment, differences between previsions, formulated in an MRI… (more)

Subjects/Keywords: Volume reconstruction; Markov Random Field; Sparsity

Record Details Similar Records

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APA (6^{th} Edition):

Plumat, J. (2012). Image classification and reconstruction using Markov Random Field modeling and sparsity. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/120111

Not specified: Masters Thesis or Doctoral Dissertation

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

Plumat, Jérôme. “Image classification and reconstruction using Markov Random Field modeling and sparsity.” 2012. Thesis, Université Catholique de Louvain. Accessed June 24, 2019. http://hdl.handle.net/2078.1/120111.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Plumat, Jérôme. “Image classification and reconstruction using Markov Random Field modeling and sparsity.” 2012. Web. 24 Jun 2019.

Vancouver:

Plumat J. Image classification and reconstruction using Markov Random Field modeling and sparsity. [Internet] [Thesis]. Université Catholique de Louvain; 2012. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/2078.1/120111.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Plumat J. Image classification and reconstruction using Markov Random Field modeling and sparsity. [Thesis]. Université Catholique de Louvain; 2012. Available from: http://hdl.handle.net/2078.1/120111

Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech

12. Lewis, Cannada Andrew. The Unreasonable Usefulness of Approximation by Linear Combination.

Degree: PhD, Chemistry, 2018, Virginia Tech

URL: http://hdl.handle.net/10919/83866

► Through the exploitation of data-*sparsity* – a catch all term for savings gained from a variety of approximations – it is possible to reduce the computational…
(more)

Subjects/Keywords: Electronic Structure; Data-Sparsity; Tensor; Reduced Scaling

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APA (6^{th} Edition):

Lewis, C. A. (2018). The Unreasonable Usefulness of Approximation by Linear Combination. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83866

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

Lewis, Cannada Andrew. “The Unreasonable Usefulness of Approximation by Linear Combination.” 2018. Doctoral Dissertation, Virginia Tech. Accessed June 24, 2019. http://hdl.handle.net/10919/83866.

MLA Handbook (7^{th} Edition):

Lewis, Cannada Andrew. “The Unreasonable Usefulness of Approximation by Linear Combination.” 2018. Web. 24 Jun 2019.

Vancouver:

Lewis CA. The Unreasonable Usefulness of Approximation by Linear Combination. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/10919/83866.

Council of Science Editors:

Lewis CA. The Unreasonable Usefulness of Approximation by Linear Combination. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83866

University of British Columbia

13. Lebed, Evgeniy. Sparse signal recovery in a transform domain .

Degree: 2008, University of British Columbia

URL: http://hdl.handle.net/2429/4171

► The ability to efficiently and sparsely represent seismic data is becoming an increasingly important problem in geophysics. Over the last thirty years many transforms such…
(more)

Subjects/Keywords: Wavelets; Transforms; Sparsity

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APA (6^{th} Edition):

Lebed, E. (2008). Sparse signal recovery in a transform domain . (Thesis). University of British Columbia. Retrieved from http://hdl.handle.net/2429/4171

Not specified: Masters Thesis or Doctoral Dissertation

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

Lebed, Evgeniy. “Sparse signal recovery in a transform domain .” 2008. Thesis, University of British Columbia. Accessed June 24, 2019. http://hdl.handle.net/2429/4171.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Lebed, Evgeniy. “Sparse signal recovery in a transform domain .” 2008. Web. 24 Jun 2019.

Vancouver:

Lebed E. Sparse signal recovery in a transform domain . [Internet] [Thesis]. University of British Columbia; 2008. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/2429/4171.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lebed E. Sparse signal recovery in a transform domain . [Thesis]. University of British Columbia; 2008. Available from: http://hdl.handle.net/2429/4171

Not specified: Masters Thesis or Doctoral Dissertation

Princeton University

14. Li, Chenchuan. Inference in Regressions with Many Controls .

Degree: PhD, 2016, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01w3763925c

► In this thesis, we consider inference on a scalar coefficient of interest in a linear regression model with many potential control variables. Without any constraint…
(more)

Subjects/Keywords: Bound; Causal; Control; Inference; Regressions; Sparsity

Record Details Similar Records

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APA (6^{th} Edition):

Li, C. (2016). Inference in Regressions with Many Controls . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01w3763925c

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

Li, Chenchuan. “Inference in Regressions with Many Controls .” 2016. Doctoral Dissertation, Princeton University. Accessed June 24, 2019. http://arks.princeton.edu/ark:/88435/dsp01w3763925c.

MLA Handbook (7^{th} Edition):

Li, Chenchuan. “Inference in Regressions with Many Controls .” 2016. Web. 24 Jun 2019.

Vancouver:

Li C. Inference in Regressions with Many Controls . [Internet] [Doctoral dissertation]. Princeton University; 2016. [cited 2019 Jun 24]. Available from: http://arks.princeton.edu/ark:/88435/dsp01w3763925c.

Council of Science Editors:

Li C. Inference in Regressions with Many Controls . [Doctoral Dissertation]. Princeton University; 2016. Available from: http://arks.princeton.edu/ark:/88435/dsp01w3763925c

University of Waterloo

15. Yang, Shenghao. Split Cuts From Sparse Disjunctions.

Degree: 2019, University of Waterloo

URL: http://hdl.handle.net/10012/14363

► Cutting planes are one of the major techniques used in solving Mixed-Integer Linear Programming (MIP) models. Various types of cuts have long been exploited by…
(more)

Subjects/Keywords: mixed-integer programming; split cuts; sparsity; decomposition

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APA (6^{th} Edition):

Yang, S. (2019). Split Cuts From Sparse Disjunctions. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14363

Not specified: Masters Thesis or Doctoral Dissertation

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

Yang, Shenghao. “Split Cuts From Sparse Disjunctions.” 2019. Thesis, University of Waterloo. Accessed June 24, 2019. http://hdl.handle.net/10012/14363.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Yang, Shenghao. “Split Cuts From Sparse Disjunctions.” 2019. Web. 24 Jun 2019.

Vancouver:

Yang S. Split Cuts From Sparse Disjunctions. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/10012/14363.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yang S. Split Cuts From Sparse Disjunctions. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14363

Not specified: Masters Thesis or Doctoral Dissertation

16. Orchard, Peter Raymond. Sparse inverse covariance estimation in Gaussian graphical models.

Degree: PhD, 2014, University of Edinburgh

URL: http://hdl.handle.net/1842/9955

► One of the fundamental tasks in science is to find explainable relationships between observed phenomena. Recent work has addressed this problem by attempting to learn…
(more)

Subjects/Keywords: sparsity; Gaussian; latent variables; copula; GWishart; HMC

Record Details Similar Records

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APA (6^{th} Edition):

Orchard, P. R. (2014). Sparse inverse covariance estimation in Gaussian graphical models. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/9955

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

Orchard, Peter Raymond. “Sparse inverse covariance estimation in Gaussian graphical models.” 2014. Doctoral Dissertation, University of Edinburgh. Accessed June 24, 2019. http://hdl.handle.net/1842/9955.

MLA Handbook (7^{th} Edition):

Orchard, Peter Raymond. “Sparse inverse covariance estimation in Gaussian graphical models.” 2014. Web. 24 Jun 2019.

Vancouver:

Orchard PR. Sparse inverse covariance estimation in Gaussian graphical models. [Internet] [Doctoral dissertation]. University of Edinburgh; 2014. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/1842/9955.

Council of Science Editors:

Orchard PR. Sparse inverse covariance estimation in Gaussian graphical models. [Doctoral Dissertation]. University of Edinburgh; 2014. Available from: http://hdl.handle.net/1842/9955

Colorado School of Mines

17.
Andrade de Almeida, Lucas José.
Seismic data interpolation using *sparsity* constrained inversion.

Degree: MS(M.S.), Geophysics, 2017, Colorado School of Mines

URL: http://hdl.handle.net/11124/171138

► Missing data reconstruction is an ongoing challenge in seismic processing for incomplete and irregular acquisition. The problem of missing data negatively affects several important processing…
(more)

Subjects/Keywords: Interpolation; Sparsity; Analysis; Synthesis; Seismic processing

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APA (6^{th} Edition):

Andrade de Almeida, L. J. (2017). Seismic data interpolation using sparsity constrained inversion. (Masters Thesis). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/171138

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

Andrade de Almeida, Lucas José. “Seismic data interpolation using sparsity constrained inversion.” 2017. Masters Thesis, Colorado School of Mines. Accessed June 24, 2019. http://hdl.handle.net/11124/171138.

MLA Handbook (7^{th} Edition):

Andrade de Almeida, Lucas José. “Seismic data interpolation using sparsity constrained inversion.” 2017. Web. 24 Jun 2019.

Vancouver:

Andrade de Almeida LJ. Seismic data interpolation using sparsity constrained inversion. [Internet] [Masters thesis]. Colorado School of Mines; 2017. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/11124/171138.

Council of Science Editors:

Andrade de Almeida LJ. Seismic data interpolation using sparsity constrained inversion. [Masters Thesis]. Colorado School of Mines; 2017. Available from: http://hdl.handle.net/11124/171138

Georgia Tech

18. Fair, Kaitlin Lindsay. A biologically plausible sparse approximation solver on neuromorphic hardware.

Degree: PhD, Electrical and Computer Engineering, 2017, Georgia Tech

URL: http://hdl.handle.net/1853/59782

► We develop a novel design methodology to map the biologically plausible Locally Competitive Algorithm (LCA) to the brain-inspired TrueNorth chip to solve for the sparse…
(more)

Subjects/Keywords: Neuromorphic; Bio-inspired; TrueNorth; Sparsity; Sparse approximation

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APA (6^{th} Edition):

Fair, K. L. (2017). A biologically plausible sparse approximation solver on neuromorphic hardware. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59782

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

Fair, Kaitlin Lindsay. “A biologically plausible sparse approximation solver on neuromorphic hardware.” 2017. Doctoral Dissertation, Georgia Tech. Accessed June 24, 2019. http://hdl.handle.net/1853/59782.

MLA Handbook (7^{th} Edition):

Fair, Kaitlin Lindsay. “A biologically plausible sparse approximation solver on neuromorphic hardware.” 2017. Web. 24 Jun 2019.

Vancouver:

Fair KL. A biologically plausible sparse approximation solver on neuromorphic hardware. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/1853/59782.

Council of Science Editors:

Fair KL. A biologically plausible sparse approximation solver on neuromorphic hardware. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59782

University of Minnesota

19. Farahmand, Shahrokh. Distributed and robust tracking by exploiting set-membership and sarsity.

Degree: 2011, University of Minnesota

URL: http://purl.umn.edu/113021

► Target tracking research and development are of major importance and continuously expanding interest to a gamut of traditional and emerging applications, which include radar- and…
(more)

Subjects/Keywords: Distributed; Robust,; Sparsity,; Trget tracking; Electrical Engineering

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APA (6^{th} Edition):

Farahmand, S. (2011). Distributed and robust tracking by exploiting set-membership and sarsity. (Thesis). University of Minnesota. Retrieved from http://purl.umn.edu/113021

Not specified: Masters Thesis or Doctoral Dissertation

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

Farahmand, Shahrokh. “Distributed and robust tracking by exploiting set-membership and sarsity.” 2011. Thesis, University of Minnesota. Accessed June 24, 2019. http://purl.umn.edu/113021.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Farahmand, Shahrokh. “Distributed and robust tracking by exploiting set-membership and sarsity.” 2011. Web. 24 Jun 2019.

Vancouver:

Farahmand S. Distributed and robust tracking by exploiting set-membership and sarsity. [Internet] [Thesis]. University of Minnesota; 2011. [cited 2019 Jun 24]. Available from: http://purl.umn.edu/113021.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Farahmand S. Distributed and robust tracking by exploiting set-membership and sarsity. [Thesis]. University of Minnesota; 2011. Available from: http://purl.umn.edu/113021

Not specified: Masters Thesis or Doctoral Dissertation

University of Minnesota

20.
Bazerque, Juan Andres.
Leveraging *sparsity* for genetic and wireless cognitive networks.

Degree: PhD, Electrical Engineering, 2013, University of Minnesota

URL: http://purl.umn.edu/157612

► Sparse graphical models can capture uncertainty of interconnected systems while promoting parsimony and simplicity - two attributes that can be utilized to identify the topology…
(more)

Subjects/Keywords: Cognitive radios; Genetics; Kernels; Networks; Sparsity; Tensors

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APA (6^{th} Edition):

Bazerque, J. A. (2013). Leveraging sparsity for genetic and wireless cognitive networks. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/157612

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

Bazerque, Juan Andres. “Leveraging sparsity for genetic and wireless cognitive networks.” 2013. Doctoral Dissertation, University of Minnesota. Accessed June 24, 2019. http://purl.umn.edu/157612.

MLA Handbook (7^{th} Edition):

Bazerque, Juan Andres. “Leveraging sparsity for genetic and wireless cognitive networks.” 2013. Web. 24 Jun 2019.

Vancouver:

Bazerque JA. Leveraging sparsity for genetic and wireless cognitive networks. [Internet] [Doctoral dissertation]. University of Minnesota; 2013. [cited 2019 Jun 24]. Available from: http://purl.umn.edu/157612.

Council of Science Editors:

Bazerque JA. Leveraging sparsity for genetic and wireless cognitive networks. [Doctoral Dissertation]. University of Minnesota; 2013. Available from: http://purl.umn.edu/157612

Cornell University

21. Niculae, Vlad. Learning Deep Models with Linguistically-Inspired Structure .

Degree: 2018, Cornell University

URL: http://hdl.handle.net/1813/59540

► Many applied machine learning tasks involve structured representations. This is particularly the case in natural language processing (NLP), where the discrete, compositional nature of words…
(more)

Subjects/Keywords: Computer science; ML; NLP; SparseMAP; sparsity; structure

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APA (6^{th} Edition):

Niculae, V. (2018). Learning Deep Models with Linguistically-Inspired Structure . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/59540

Not specified: Masters Thesis or Doctoral Dissertation

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

Niculae, Vlad. “Learning Deep Models with Linguistically-Inspired Structure .” 2018. Thesis, Cornell University. Accessed June 24, 2019. http://hdl.handle.net/1813/59540.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Niculae, Vlad. “Learning Deep Models with Linguistically-Inspired Structure .” 2018. Web. 24 Jun 2019.

Vancouver:

Niculae V. Learning Deep Models with Linguistically-Inspired Structure . [Internet] [Thesis]. Cornell University; 2018. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/1813/59540.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Niculae V. Learning Deep Models with Linguistically-Inspired Structure . [Thesis]. Cornell University; 2018. Available from: http://hdl.handle.net/1813/59540

Not specified: Masters Thesis or Doctoral Dissertation

Duquesne University

22. Sano, Teresa. Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising.

Degree: MS, Computational Mathematics, 2009, Duquesne University

URL: https://dsc.duq.edu/etd/1145

► Digital image denoising is a widely know problem in image processing. In this work, we focus on removing additive white Gaussian noise from a given…
(more)

Subjects/Keywords: image processing; denoising; sparsity; geometric features

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APA (6^{th} Edition):

Sano, T. (2009). Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising. (Masters Thesis). Duquesne University. Retrieved from https://dsc.duq.edu/etd/1145

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

Sano, Teresa. “Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising.” 2009. Masters Thesis, Duquesne University. Accessed June 24, 2019. https://dsc.duq.edu/etd/1145.

MLA Handbook (7^{th} Edition):

Sano, Teresa. “Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising.” 2009. Web. 24 Jun 2019.

Vancouver:

Sano T. Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising. [Internet] [Masters thesis]. Duquesne University; 2009. [cited 2019 Jun 24]. Available from: https://dsc.duq.edu/etd/1145.

Council of Science Editors:

Sano T. Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising. [Masters Thesis]. Duquesne University; 2009. Available from: https://dsc.duq.edu/etd/1145

Princeton University

23. Bastian, Caleb Deen. Analysis of Multivariate High-Dimensional Complex Systems and Applications .

Degree: PhD, 2016, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp018k71nk579

► Complex systems are those having interactions among system states, where states are each considered as input or output variables. These interactions can produce diverse phenomena,…
(more)

Subjects/Keywords: Complex System; EMT; HDMR; MIMO; Sparsity

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APA (6^{th} Edition):

Bastian, C. D. (2016). Analysis of Multivariate High-Dimensional Complex Systems and Applications . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp018k71nk579

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

Bastian, Caleb Deen. “Analysis of Multivariate High-Dimensional Complex Systems and Applications .” 2016. Doctoral Dissertation, Princeton University. Accessed June 24, 2019. http://arks.princeton.edu/ark:/88435/dsp018k71nk579.

MLA Handbook (7^{th} Edition):

Bastian, Caleb Deen. “Analysis of Multivariate High-Dimensional Complex Systems and Applications .” 2016. Web. 24 Jun 2019.

Vancouver:

Bastian CD. Analysis of Multivariate High-Dimensional Complex Systems and Applications . [Internet] [Doctoral dissertation]. Princeton University; 2016. [cited 2019 Jun 24]. Available from: http://arks.princeton.edu/ark:/88435/dsp018k71nk579.

Council of Science Editors:

Bastian CD. Analysis of Multivariate High-Dimensional Complex Systems and Applications . [Doctoral Dissertation]. Princeton University; 2016. Available from: http://arks.princeton.edu/ark:/88435/dsp018k71nk579

Penn State University

24. Mo, Xuan. adaptive sparse representations for video anomaly detection.

Degree: PhD, Electrical Engineering, 2014, Penn State University

URL: https://etda.libraries.psu.edu/catalog/22603

► Video surveillance systems are widely used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and…
(more)

Subjects/Keywords: video anomaly detection; sparsity model; kernel function; multi-object; low rank sparsity prior; outlier rejection

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APA (6^{th} Edition):

Mo, X. (2014). adaptive sparse representations for video anomaly detection. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/22603

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

Mo, Xuan. “adaptive sparse representations for video anomaly detection.” 2014. Doctoral Dissertation, Penn State University. Accessed June 24, 2019. https://etda.libraries.psu.edu/catalog/22603.

MLA Handbook (7^{th} Edition):

Mo, Xuan. “adaptive sparse representations for video anomaly detection.” 2014. Web. 24 Jun 2019.

Vancouver:

Mo X. adaptive sparse representations for video anomaly detection. [Internet] [Doctoral dissertation]. Penn State University; 2014. [cited 2019 Jun 24]. Available from: https://etda.libraries.psu.edu/catalog/22603.

Council of Science Editors:

Mo X. adaptive sparse representations for video anomaly detection. [Doctoral Dissertation]. Penn State University; 2014. Available from: https://etda.libraries.psu.edu/catalog/22603

25. Auría Rasclosa, Anna. Sparse inverse problems for Fourier imaging: applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging.

Degree: 2017, EPFL

URL: http://infoscience.epfl.ch/record/227089

► Many natural images have low intrinsic dimension (a.k.a. sparse), meaning that they can be represented with very few coefficients when expressed in an adequate domain.…
(more)

Subjects/Keywords: inverse problems; compressed sensing; sparsity; structured sparsity; convex optimization; optical interferometry; diffusion MRI; spherical deconvolution; HARDI; microstructure imaging.

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APA (6^{th} Edition):

Auría Rasclosa, A. (2017). Sparse inverse problems for Fourier imaging: applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/227089

Not specified: Masters Thesis or Doctoral Dissertation

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

Auría Rasclosa, Anna. “Sparse inverse problems for Fourier imaging: applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging.” 2017. Thesis, EPFL. Accessed June 24, 2019. http://infoscience.epfl.ch/record/227089.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Auría Rasclosa, Anna. “Sparse inverse problems for Fourier imaging: applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging.” 2017. Web. 24 Jun 2019.

Vancouver:

Auría Rasclosa A. Sparse inverse problems for Fourier imaging: applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging. [Internet] [Thesis]. EPFL; 2017. [cited 2019 Jun 24]. Available from: http://infoscience.epfl.ch/record/227089.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Auría Rasclosa A. Sparse inverse problems for Fourier imaging: applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging. [Thesis]. EPFL; 2017. Available from: http://infoscience.epfl.ch/record/227089

Not specified: Masters Thesis or Doctoral Dissertation

Washington University in St. Louis

26. Tang, Gongguo. Computable Performance Analysis of Recovering Signals with Low-dimensional Structures.

Degree: PhD, Electrical and Systems Engineering, 2011, Washington University in St. Louis

URL: https://openscholarship.wustl.edu/etd/647

► The last decade witnessed the burgeoning development in the reconstruction of signals by exploiting their low-dimensional structures, particularly, the *sparsity*, the block-*sparsity*, the low-rankness, and…
(more)

Subjects/Keywords: Electrical Engineering; Statistics; Mathematics; block-sparsity recovery; compressive sensing; fixed point theory; low-rank matrix recovery; semidefinite programming; sparsity recovery

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

APA (6^{th} Edition):

Tang, G. (2011). Computable Performance Analysis of Recovering Signals with Low-dimensional Structures. (Doctoral Dissertation). Washington University in St. Louis. Retrieved from https://openscholarship.wustl.edu/etd/647

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

Tang, Gongguo. “Computable Performance Analysis of Recovering Signals with Low-dimensional Structures.” 2011. Doctoral Dissertation, Washington University in St. Louis. Accessed June 24, 2019. https://openscholarship.wustl.edu/etd/647.

MLA Handbook (7^{th} Edition):

Tang, Gongguo. “Computable Performance Analysis of Recovering Signals with Low-dimensional Structures.” 2011. Web. 24 Jun 2019.

Vancouver:

Tang G. Computable Performance Analysis of Recovering Signals with Low-dimensional Structures. [Internet] [Doctoral dissertation]. Washington University in St. Louis; 2011. [cited 2019 Jun 24]. Available from: https://openscholarship.wustl.edu/etd/647.

Council of Science Editors:

Tang G. Computable Performance Analysis of Recovering Signals with Low-dimensional Structures. [Doctoral Dissertation]. Washington University in St. Louis; 2011. Available from: https://openscholarship.wustl.edu/etd/647

27.
Vinyes, Marina.
Convex matrix *sparsity* for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques.

Degree: Docteur es, Signal, Image, Automatique, 2018, Université Paris-Est

URL: http://www.theses.fr/2018PESC1130

►

En apprentissage automatique on a pour but d'apprendre un modèle, à partir de données, qui soit capable de faire des prédictions sur des nouvelles données… (more)

Subjects/Keywords: Normes atomiques; Optimisation convexe; Parcimonie matricielle; Rang faible; Parcimonie; Atomic norms; Convex optimisation; Matrix sparsity; Low rank; Sparsity

Record Details Similar Records

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

APA (6^{th} Edition):

Vinyes, M. (2018). Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques. (Doctoral Dissertation). Université Paris-Est. Retrieved from http://www.theses.fr/2018PESC1130

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

Vinyes, Marina. “Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques.” 2018. Doctoral Dissertation, Université Paris-Est. Accessed June 24, 2019. http://www.theses.fr/2018PESC1130.

MLA Handbook (7^{th} Edition):

Vinyes, Marina. “Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques.” 2018. Web. 24 Jun 2019.

Vancouver:

Vinyes M. Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques. [Internet] [Doctoral dissertation]. Université Paris-Est; 2018. [cited 2019 Jun 24]. Available from: http://www.theses.fr/2018PESC1130.

Council of Science Editors:

Vinyes M. Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques. [Doctoral Dissertation]. Université Paris-Est; 2018. Available from: http://www.theses.fr/2018PESC1130

University of Ontario Institute of Technology

28. Azimipanah, Aras. Compressive sensing based non-destructive testing using ultrasonic arrays.

Degree: 2013, University of Ontario Institute of Technology

URL: http://hdl.handle.net/10155/351

► In this thesis, we apply compressive sensing approach and the notion of sparse signal recovery to the non-destructive testing application, using ultrasonic arrays. In many…
(more)

Subjects/Keywords: Compressive sensing; Sparsity; Non-destructive testing; Array signal processing; Ultrasound imaging

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APA (6^{th} Edition):

Azimipanah, A. (2013). Compressive sensing based non-destructive testing using ultrasonic arrays. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/351

Not specified: Masters Thesis or Doctoral Dissertation

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

Azimipanah, Aras. “Compressive sensing based non-destructive testing using ultrasonic arrays.” 2013. Thesis, University of Ontario Institute of Technology. Accessed June 24, 2019. http://hdl.handle.net/10155/351.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Azimipanah, Aras. “Compressive sensing based non-destructive testing using ultrasonic arrays.” 2013. Web. 24 Jun 2019.

Vancouver:

Azimipanah A. Compressive sensing based non-destructive testing using ultrasonic arrays. [Internet] [Thesis]. University of Ontario Institute of Technology; 2013. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/10155/351.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Azimipanah A. Compressive sensing based non-destructive testing using ultrasonic arrays. [Thesis]. University of Ontario Institute of Technology; 2013. Available from: http://hdl.handle.net/10155/351

Not specified: Masters Thesis or Doctoral Dissertation

University of Colorado

29.
Kaslovsky, Daniel N.
Geometric *Sparsity* in High Dimension.

Degree: PhD, Mathematics, 2012, University of Colorado

URL: http://scholar.colorado.edu/math_gradetds/15

► While typically complex and high-dimensional, modern data sets often have a concise underlying structure. This thesis explores the *sparsity* inherent in the geometric structure…
(more)

Subjects/Keywords: Geometry; High-dimensional data; Noise; Sparsity; Applied Mathematics

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APA (6^{th} Edition):

Kaslovsky, D. N. (2012). Geometric Sparsity in High Dimension. (Doctoral Dissertation). University of Colorado. Retrieved from http://scholar.colorado.edu/math_gradetds/15

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

Kaslovsky, Daniel N. “Geometric Sparsity in High Dimension.” 2012. Doctoral Dissertation, University of Colorado. Accessed June 24, 2019. http://scholar.colorado.edu/math_gradetds/15.

MLA Handbook (7^{th} Edition):

Kaslovsky, Daniel N. “Geometric Sparsity in High Dimension.” 2012. Web. 24 Jun 2019.

Vancouver:

Kaslovsky DN. Geometric Sparsity in High Dimension. [Internet] [Doctoral dissertation]. University of Colorado; 2012. [cited 2019 Jun 24]. Available from: http://scholar.colorado.edu/math_gradetds/15.

Council of Science Editors:

Kaslovsky DN. Geometric Sparsity in High Dimension. [Doctoral Dissertation]. University of Colorado; 2012. Available from: http://scholar.colorado.edu/math_gradetds/15

Syracuse University

30.
Liu, Sijia.
Resource Management for Distributed Estimation via *Sparsity*-Promoting Regularization.

Degree: PhD, Electrical Engineering and Computer Science, 2016, Syracuse University

URL: https://surface.syr.edu/etd/441

► Recent advances in wireless communications and electronics have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate…
(more)

Subjects/Keywords: convex optimization; distributed estimation; resource management; sparsity; wireless sensor networks; Engineering

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

APA (6^{th} Edition):

Liu, S. (2016). Resource Management for Distributed Estimation via Sparsity-Promoting Regularization. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/441

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

Liu, Sijia. “Resource Management for Distributed Estimation via Sparsity-Promoting Regularization.” 2016. Doctoral Dissertation, Syracuse University. Accessed June 24, 2019. https://surface.syr.edu/etd/441.

MLA Handbook (7^{th} Edition):

Liu, Sijia. “Resource Management for Distributed Estimation via Sparsity-Promoting Regularization.” 2016. Web. 24 Jun 2019.

Vancouver:

Liu S. Resource Management for Distributed Estimation via Sparsity-Promoting Regularization. [Internet] [Doctoral dissertation]. Syracuse University; 2016. [cited 2019 Jun 24]. Available from: https://surface.syr.edu/etd/441.

Council of Science Editors:

Liu S. Resource Management for Distributed Estimation via Sparsity-Promoting Regularization. [Doctoral Dissertation]. Syracuse University; 2016. Available from: https://surface.syr.edu/etd/441