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Showing records 1 – 30 of
339 total matches.

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Dates

- 2017 – 2021 (140)
- 2012 – 2016 (170)
- 2007 – 2011 (42)

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Department

- Statistics (22)
- Electrical Engineering (14)
- Traitement du signal et des images (10)

Degrees

- PhD (116)
- Docteur es (74)
- MS (18)

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UCLA

1.
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

Record Details Similar Records

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

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

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):

Flynn, John Joseph. “Learning a simplicial structure using sparsity.” 2014. Thesis, UCLA. Accessed March 01, 2021. http://www.escholarship.org/uc/item/52v7g1sp.

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Flynn, John Joseph. “Learning a simplicial structure using sparsity.” 2014. Web. 01 Mar 2021.

Vancouver:

Flynn JJ. Learning a simplicial structure using sparsity. [Internet] [Thesis]. UCLA; 2014. [cited 2021 Mar 01]. Available from: http://www.escholarship.org/uc/item/52v7g1sp.

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

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

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

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 March 01, 2021. 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. 01 Mar 2021.

Vancouver:

Phillipson KR. Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems. [Internet] [Doctoral dissertation]. Texas A&M University; 2016. [cited 2021 Mar 01]. 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

Texas A&M University

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

Degree: MS, Electrical Engineering, 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

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

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

Apaydin, Meltem. “Phase Retrieval of Sparse Signals from Magnitude Information.” 2014. Masters Thesis, Texas A&M University. Accessed March 01, 2021. http://hdl.handle.net/1969.1/153388.

MLA Handbook (7^{th} Edition):

Apaydin, Meltem. “Phase Retrieval of Sparse Signals from Magnitude Information.” 2014. Web. 01 Mar 2021.

Vancouver:

Apaydin M. Phase Retrieval of Sparse Signals from Magnitude Information. [Internet] [Masters thesis]. Texas A&M University; 2014. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/1969.1/153388.

Council of Science Editors:

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

Penn State University

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

Degree: 2014, Penn State University

URL: https://submit-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

Record Details Similar Records

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

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

Tutuk, Fatih. “Sparse Linear Time Invariant System Identification Using Weighted Lasso.” 2014. Thesis, Penn State University. Accessed March 01, 2021. https://submit-etda.libraries.psu.edu/catalog/23710.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Tutuk, Fatih. “Sparse Linear Time Invariant System Identification Using Weighted Lasso.” 2014. Web. 01 Mar 2021.

Vancouver:

Tutuk F. Sparse Linear Time Invariant System Identification Using Weighted Lasso. [Internet] [Thesis]. Penn State University; 2014. [cited 2021 Mar 01]. Available from: https://submit-etda.libraries.psu.edu/catalog/23710.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

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

Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

5. Tang, Yajie (author). Bayesian Learning Applied to Radio Astronomy Image Formation.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:eebfc5bb-1393-477c-8437-8e51457ae6d7

►

Radio astronomy image formation can be treated as a linear inverse problem. However, due to physical limitations, this inverse problem is ill-posed. To overcome the… (more)

Subjects/Keywords: Bayesian learning; Radio astronomy; Sparsity

Record Details Similar Records

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

APA (6^{th} Edition):

Tang, Y. (. (2019). Bayesian Learning Applied to Radio Astronomy Image Formation. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:eebfc5bb-1393-477c-8437-8e51457ae6d7

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

Tang, Yajie (author). “Bayesian Learning Applied to Radio Astronomy Image Formation.” 2019. Masters Thesis, Delft University of Technology. Accessed March 01, 2021. http://resolver.tudelft.nl/uuid:eebfc5bb-1393-477c-8437-8e51457ae6d7.

MLA Handbook (7^{th} Edition):

Tang, Yajie (author). “Bayesian Learning Applied to Radio Astronomy Image Formation.” 2019. Web. 01 Mar 2021.

Vancouver:

Tang Y(. Bayesian Learning Applied to Radio Astronomy Image Formation. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 01]. Available from: http://resolver.tudelft.nl/uuid:eebfc5bb-1393-477c-8437-8e51457ae6d7.

Council of Science Editors:

Tang Y(. Bayesian Learning Applied to Radio Astronomy Image Formation. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:eebfc5bb-1393-477c-8437-8e51457ae6d7

University of Illinois – Urbana-Champaign

6.
Zhu, Rongda.
Exploiting *sparsity* for machine learning in big data.

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

URL: http://hdl.handle.net/2142/97598

► The rapid development of modern information technology has significantly facilitated the generation, collection, transmission and storage of all kinds of data. With the so-called “big…
(more)

Subjects/Keywords: Sparsity; Machine learning; Big data

Record Details Similar Records

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

APA (6^{th} Edition):

Zhu, R. (2017). Exploiting sparsity for machine learning in big data. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/97598

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

Zhu, Rongda. “Exploiting sparsity for machine learning in big data.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed March 01, 2021. http://hdl.handle.net/2142/97598.

MLA Handbook (7^{th} Edition):

Zhu, Rongda. “Exploiting sparsity for machine learning in big data.” 2017. Web. 01 Mar 2021.

Vancouver:

Zhu R. Exploiting sparsity for machine learning in big data. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/2142/97598.

Council of Science Editors:

Zhu R. Exploiting sparsity for machine learning in big data. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/97598

University of New Mexico

7.
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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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 March 01, 2021. http://hdl.handle.net/1928/24336.

MLA Handbook (7^{th} Edition):

Potluru, Vamsi. “Matrix Factorization: Nonnegativity, Sparsity and Independence.” 2014. Web. 01 Mar 2021.

Vancouver:

Potluru V. Matrix Factorization: Nonnegativity, Sparsity and Independence. [Internet] [Doctoral dissertation]. University of New Mexico; 2014. [cited 2021 Mar 01]. 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 Ontario Institute of Technology

8. 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

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 March 01, 2021. http://hdl.handle.net/10155/672.

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. 01 Mar 2021.

Vancouver:

Hamidi S. Sparse signal representation based algorithms with application to ultrasonic array imaging. [Internet] [Thesis]. University of Ontario Institute of Technology; 2016. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/10155/672.

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

University of Alberta

9.
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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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 March 01, 2021. 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. 01 Mar 2021.

Vancouver:

Bonar CD. Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data. [Internet] [Masters thesis]. University of Alberta; 2012. [cited 2021 Mar 01]. 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 Waterloo

10. 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

Record Details Similar Records

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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 March 01, 2021. 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. 01 Mar 2021.

Vancouver:

Yang S. Split Cuts From Sparse Disjunctions. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2021 Mar 01]. 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

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

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 March 01, 2021. 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. 01 Mar 2021.

Vancouver:

Plumat J. Image classification and reconstruction using Markov Random Field modeling and sparsity. [Internet] [Thesis]. Université Catholique de Louvain; 2012. [cited 2021 Mar 01]. 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

University of Washington

12. 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

Record Details Similar Records

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

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 March 01, 2021. http://hdl.handle.net/1773/43162.

MLA Handbook (7^{th} Edition):

Tank, Alex. “Discovering Interactions in Multivariate Time Series.” 2018. Web. 01 Mar 2021.

Vancouver:

Tank A. Discovering Interactions in Multivariate Time Series. [Internet] [Doctoral dissertation]. University of Washington; 2018. [cited 2021 Mar 01]. 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

Delft University of Technology

13.
Zwart, Joost (author).
* Sparsity* based hybrid system identification using a SAT solver.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:725afc81-eefb-47b2-ac78-b6119a86eae8

►

System identification for switched linear systems from input output data has received substantial attention in recent years. There is a growing interest for techniques that… (more)

Subjects/Keywords: SAT solver; Hybrid Systems; Identification; Sparsity

Record Details Similar Records

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

APA (6^{th} Edition):

Zwart, J. (. (2019). Sparsity based hybrid system identification using a SAT solver. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:725afc81-eefb-47b2-ac78-b6119a86eae8

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

Zwart, Joost (author). “Sparsity based hybrid system identification using a SAT solver.” 2019. Masters Thesis, Delft University of Technology. Accessed March 01, 2021. http://resolver.tudelft.nl/uuid:725afc81-eefb-47b2-ac78-b6119a86eae8.

MLA Handbook (7^{th} Edition):

Zwart, Joost (author). “Sparsity based hybrid system identification using a SAT solver.” 2019. Web. 01 Mar 2021.

Vancouver:

Zwart J(. Sparsity based hybrid system identification using a SAT solver. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 01]. Available from: http://resolver.tudelft.nl/uuid:725afc81-eefb-47b2-ac78-b6119a86eae8.

Council of Science Editors:

Zwart J(. Sparsity based hybrid system identification using a SAT solver. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:725afc81-eefb-47b2-ac78-b6119a86eae8

Colorado School of Mines

14.
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

Record Details Similar Records

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

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 March 01, 2021. 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. 01 Mar 2021.

Vancouver:

Andrade de Almeida LJ. Seismic data interpolation using sparsity constrained inversion. [Internet] [Masters thesis]. Colorado School of Mines; 2017. [cited 2021 Mar 01]. 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

University of Minnesota

15. 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

Record Details Similar Records

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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 March 01, 2021. 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. 01 Mar 2021.

Vancouver:

Farahmand S. Distributed and robust tracking by exploiting set-membership and sarsity. [Internet] [Thesis]. University of Minnesota; 2011. [cited 2021 Mar 01]. 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

16.
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

Record Details Similar Records

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

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 March 01, 2021. http://purl.umn.edu/157612.

MLA Handbook (7^{th} Edition):

Bazerque, Juan Andres. “Leveraging sparsity for genetic and wireless cognitive networks.” 2013. Web. 01 Mar 2021.

Vancouver:

Bazerque JA. Leveraging sparsity for genetic and wireless cognitive networks. [Internet] [Doctoral dissertation]. University of Minnesota; 2013. [cited 2021 Mar 01]. 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

Clemson University

17.
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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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 March 01, 2021. https://tigerprints.clemson.edu/all_dissertations/1000.

MLA Handbook (7^{th} Edition):

Cooper, John. “Sparsity Regularization in Diffuse Optical Tomography.” 2012. Web. 01 Mar 2021.

Vancouver:

Cooper J. Sparsity Regularization in Diffuse Optical Tomography. [Internet] [Doctoral dissertation]. Clemson University; 2012. [cited 2021 Mar 01]. 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

Virginia Tech

18. 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

Record Details Similar Records

❌

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

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 March 01, 2021. http://hdl.handle.net/10919/83866.

MLA Handbook (7^{th} Edition):

Lewis, Cannada Andrew. “The Unreasonable Usefulness of Approximation by Linear Combination.” 2018. Web. 01 Mar 2021.

Vancouver:

Lewis CA. The Unreasonable Usefulness of Approximation by Linear Combination. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2021 Mar 01]. 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

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

Degree: MS- MSc, Mathematics, 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

Record Details Similar Records

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

APA (6^{th} Edition):

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

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

Lebed, Evgeniy. “Sparse signal recovery in a transform domain.” 2008. Masters Thesis, University of British Columbia. Accessed March 01, 2021. http://hdl.handle.net/2429/4171.

MLA Handbook (7^{th} Edition):

Lebed, Evgeniy. “Sparse signal recovery in a transform domain.” 2008. Web. 01 Mar 2021.

Vancouver:

Lebed E. Sparse signal recovery in a transform domain. [Internet] [Masters thesis]. University of British Columbia; 2008. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/2429/4171.

Council of Science Editors:

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

Princeton University

20. 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

Record Details Similar Records

❌

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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 March 01, 2021. 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. 01 Mar 2021.

Vancouver:

Bastian CD. Analysis of Multivariate High-Dimensional Complex Systems and Applications . [Internet] [Doctoral dissertation]. Princeton University; 2016. [cited 2021 Mar 01]. 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

Princeton University

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

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 March 01, 2021. http://arks.princeton.edu/ark:/88435/dsp01w3763925c.

MLA Handbook (7^{th} Edition):

Li, Chenchuan. “Inference in Regressions with Many Controls .” 2016. Web. 01 Mar 2021.

Vancouver:

Li C. Inference in Regressions with Many Controls . [Internet] [Doctoral dissertation]. Princeton University; 2016. [cited 2021 Mar 01]. 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

Purdue University

22. Chavez Casillas, Jonathan Allan. STOCHASTIC MODELING OF LIMIT ORDER BOOKS: CONVERGENCE OF THE PRICE PROCESS, SIMULATION AND APPLICATIONS.

Degree: PhD, Mathematics, 2015, Purdue University

URL: https://docs.lib.purdue.edu/open_access_dissertations/1342

► In the past two decades, electronic limit order books (LOBs) have become the most important mechanism through which securities are traded. A LOB contains the…
(more)

Subjects/Keywords: Level I; Limit Order Book; Sparsity

Record Details Similar Records

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

APA (6^{th} Edition):

Chavez Casillas, J. A. (2015). STOCHASTIC MODELING OF LIMIT ORDER BOOKS: CONVERGENCE OF THE PRICE PROCESS, SIMULATION AND APPLICATIONS. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/1342

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

Chavez Casillas, Jonathan Allan. “STOCHASTIC MODELING OF LIMIT ORDER BOOKS: CONVERGENCE OF THE PRICE PROCESS, SIMULATION AND APPLICATIONS.” 2015. Doctoral Dissertation, Purdue University. Accessed March 01, 2021. https://docs.lib.purdue.edu/open_access_dissertations/1342.

MLA Handbook (7^{th} Edition):

Chavez Casillas, Jonathan Allan. “STOCHASTIC MODELING OF LIMIT ORDER BOOKS: CONVERGENCE OF THE PRICE PROCESS, SIMULATION AND APPLICATIONS.” 2015. Web. 01 Mar 2021.

Vancouver:

Chavez Casillas JA. STOCHASTIC MODELING OF LIMIT ORDER BOOKS: CONVERGENCE OF THE PRICE PROCESS, SIMULATION AND APPLICATIONS. [Internet] [Doctoral dissertation]. Purdue University; 2015. [cited 2021 Mar 01]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1342.

Council of Science Editors:

Chavez Casillas JA. STOCHASTIC MODELING OF LIMIT ORDER BOOKS: CONVERGENCE OF THE PRICE PROCESS, SIMULATION AND APPLICATIONS. [Doctoral Dissertation]. Purdue University; 2015. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1342

Universidade Estadual de Campinas

23. Oliveira, Henrique Evangelista de, 1987-. Identificação dos parâmetros da integral de Choquet via uma abordagem baseada em processamento de sinais esparsos: Identification of the Choquet integral parameters by means of sparse modeling.

Degree: 2020, Universidade Estadual de Campinas

URL: http://repositorio.unicamp.br/jspui/handle/REPOSIP/354771

► Abstract: The Choquet integral has been used as an aggregation operator in the field of multiple criteria decision aiding. Due to its nonlinear nature, the…
(more)

Subjects/Keywords: Estimativa de parâmetro; Esparsidade; Parameter estimation; Sparsity

Record Details Similar Records

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

APA (6^{th} Edition):

Oliveira, Henrique Evangelista de, 1. (2020). Identificação dos parâmetros da integral de Choquet via uma abordagem baseada em processamento de sinais esparsos: Identification of the Choquet integral parameters by means of sparse modeling. (Thesis). Universidade Estadual de Campinas. Retrieved from http://repositorio.unicamp.br/jspui/handle/REPOSIP/354771

Not specified: Masters Thesis or Doctoral Dissertation

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

Oliveira, Henrique Evangelista de, 1987-. “Identificação dos parâmetros da integral de Choquet via uma abordagem baseada em processamento de sinais esparsos: Identification of the Choquet integral parameters by means of sparse modeling.” 2020. Thesis, Universidade Estadual de Campinas. Accessed March 01, 2021. http://repositorio.unicamp.br/jspui/handle/REPOSIP/354771.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Oliveira, Henrique Evangelista de, 1987-. “Identificação dos parâmetros da integral de Choquet via uma abordagem baseada em processamento de sinais esparsos: Identification of the Choquet integral parameters by means of sparse modeling.” 2020. Web. 01 Mar 2021.

Vancouver:

Oliveira, Henrique Evangelista de 1. Identificação dos parâmetros da integral de Choquet via uma abordagem baseada em processamento de sinais esparsos: Identification of the Choquet integral parameters by means of sparse modeling. [Internet] [Thesis]. Universidade Estadual de Campinas; 2020. [cited 2021 Mar 01]. Available from: http://repositorio.unicamp.br/jspui/handle/REPOSIP/354771.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Oliveira, Henrique Evangelista de 1. Identificação dos parâmetros da integral de Choquet via uma abordagem baseada em processamento de sinais esparsos: Identification of the Choquet integral parameters by means of sparse modeling. [Thesis]. Universidade Estadual de Campinas; 2020. Available from: http://repositorio.unicamp.br/jspui/handle/REPOSIP/354771

Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

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

Degree: 2014, Penn State University

URL: https://submit-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

Record Details Similar Records

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

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

Mo, Xuan. “adaptive sparse representations for video anomaly detection.” 2014. Thesis, Penn State University. Accessed March 01, 2021. https://submit-etda.libraries.psu.edu/catalog/22603.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Mo, Xuan. “adaptive sparse representations for video anomaly detection.” 2014. Web. 01 Mar 2021.

Vancouver:

Mo X. adaptive sparse representations for video anomaly detection. [Internet] [Thesis]. Penn State University; 2014. [cited 2021 Mar 01]. Available from: https://submit-etda.libraries.psu.edu/catalog/22603.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

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

Not specified: Masters Thesis or Doctoral Dissertation

25.
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 March 01, 2021. 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. 01 Mar 2021.

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 2021 Mar 01]. 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

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

Record Details Similar Records

❌

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 March 01, 2021. 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. 01 Mar 2021.

Vancouver:

Tang G. Computable Performance Analysis of Recovering Signals with Low-dimensional Structures. [Internet] [Doctoral dissertation]. Washington University in St. Louis; 2011. [cited 2021 Mar 01]. 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

NSYSU

27. Lu, Chia-Ju. Item-level Trust-based Collaborative Filtering Approach to Recommender Systems.

Degree: Master, Information Management, 2008, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0723108-124134

► With the rapid growth of Internet, more and more information is disseminated in the World Wide Web. It is therefore not an easy task to…
(more)

Subjects/Keywords: recommender systems; collaborative filtering; sparsity; item-based CF; trust-based CF

Record Details Similar Records

❌

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

APA (6^{th} Edition):

Lu, C. (2008). Item-level Trust-based Collaborative Filtering Approach to Recommender Systems. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0723108-124134

Not specified: Masters Thesis or Doctoral Dissertation

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

Lu, Chia-Ju. “Item-level Trust-based Collaborative Filtering Approach to Recommender Systems.” 2008. Thesis, NSYSU. Accessed March 01, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0723108-124134.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Lu, Chia-Ju. “Item-level Trust-based Collaborative Filtering Approach to Recommender Systems.” 2008. Web. 01 Mar 2021.

Vancouver:

Lu C. Item-level Trust-based Collaborative Filtering Approach to Recommender Systems. [Internet] [Thesis]. NSYSU; 2008. [cited 2021 Mar 01]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0723108-124134.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lu C. Item-level Trust-based Collaborative Filtering Approach to Recommender Systems. [Thesis]. NSYSU; 2008. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0723108-124134

Not specified: Masters Thesis or Doctoral Dissertation

University of Alberta

28. Phillips, Seth William. Coding Techniques to Reduce Material Saturation in Holographic Data Storage.

Degree: PhD, Department of Electrical and Computer Engineering, 2014, University of Alberta

URL: https://era.library.ualberta.ca/files/t722h982q

► Holographic data storage (HDS) is an emerging data storage technology that has received attention due to a high theoretical data capacity, fast readout times, and…
(more)

Subjects/Keywords: Saturation; Sparsity; Guided Scrambling; Holographic Storage; Channel Coding; Phase Masking

Record Details Similar Records

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

APA (6^{th} Edition):

Phillips, S. W. (2014). Coding Techniques to Reduce Material Saturation in Holographic Data Storage. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/t722h982q

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

Phillips, Seth William. “Coding Techniques to Reduce Material Saturation in Holographic Data Storage.” 2014. Doctoral Dissertation, University of Alberta. Accessed March 01, 2021. https://era.library.ualberta.ca/files/t722h982q.

MLA Handbook (7^{th} Edition):

Phillips, Seth William. “Coding Techniques to Reduce Material Saturation in Holographic Data Storage.” 2014. Web. 01 Mar 2021.

Vancouver:

Phillips SW. Coding Techniques to Reduce Material Saturation in Holographic Data Storage. [Internet] [Doctoral dissertation]. University of Alberta; 2014. [cited 2021 Mar 01]. Available from: https://era.library.ualberta.ca/files/t722h982q.

Council of Science Editors:

Phillips SW. Coding Techniques to Reduce Material Saturation in Holographic Data Storage. [Doctoral Dissertation]. University of Alberta; 2014. Available from: https://era.library.ualberta.ca/files/t722h982q

Carnegie Mellon University

29. Wytock, Matt. Optimizing Optimization: Scalable Convex Programming with Proximal Operators.

Degree: 2016, Carnegie Mellon University

URL: http://repository.cmu.edu/dissertations/785

► Convex optimization has developed a wide variety of useful tools critical to many applications in machine learning. However, unlike linear and quadratic programming, general convex…
(more)

Subjects/Keywords: convex optimization; proximal operator; operator splitting; Newton method; sparsity; graphical model

Record Details Similar Records

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

APA (6^{th} Edition):

Wytock, M. (2016). Optimizing Optimization: Scalable Convex Programming with Proximal Operators. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/785

Not specified: Masters Thesis or Doctoral Dissertation

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

Wytock, Matt. “Optimizing Optimization: Scalable Convex Programming with Proximal Operators.” 2016. Thesis, Carnegie Mellon University. Accessed March 01, 2021. http://repository.cmu.edu/dissertations/785.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Wytock, Matt. “Optimizing Optimization: Scalable Convex Programming with Proximal Operators.” 2016. Web. 01 Mar 2021.

Vancouver:

Wytock M. Optimizing Optimization: Scalable Convex Programming with Proximal Operators. [Internet] [Thesis]. Carnegie Mellon University; 2016. [cited 2021 Mar 01]. Available from: http://repository.cmu.edu/dissertations/785.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wytock M. Optimizing Optimization: Scalable Convex Programming with Proximal Operators. [Thesis]. Carnegie Mellon University; 2016. Available from: http://repository.cmu.edu/dissertations/785

Not specified: Masters Thesis or Doctoral Dissertation

UCLA

30. Marchetti, Yuliya. Solution Path Clustering with Minimax Concave Penalty and Its Applications to Noisy Big Data.

Degree: Statistics, 2014, UCLA

URL: http://www.escholarship.org/uc/item/0cr9523k

► Fast accumulation of large amounts of complex data has created a needfor more sophisticated statistical methodologies to discoverinteresting patterns and better extract information from these…
(more)

Subjects/Keywords: Statistics; big data; clustering; coordinate descent; MM algorithm; regularization; sparsity

Record Details Similar Records

❌

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

APA (6^{th} Edition):

Marchetti, Y. (2014). Solution Path Clustering with Minimax Concave Penalty and Its Applications to Noisy Big Data. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/0cr9523k

Not specified: Masters Thesis or Doctoral Dissertation

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

Marchetti, Yuliya. “Solution Path Clustering with Minimax Concave Penalty and Its Applications to Noisy Big Data.” 2014. Thesis, UCLA. Accessed March 01, 2021. http://www.escholarship.org/uc/item/0cr9523k.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Marchetti, Yuliya. “Solution Path Clustering with Minimax Concave Penalty and Its Applications to Noisy Big Data.” 2014. Web. 01 Mar 2021.

Vancouver:

Marchetti Y. Solution Path Clustering with Minimax Concave Penalty and Its Applications to Noisy Big Data. [Internet] [Thesis]. UCLA; 2014. [cited 2021 Mar 01]. Available from: http://www.escholarship.org/uc/item/0cr9523k.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Marchetti Y. Solution Path Clustering with Minimax Concave Penalty and Its Applications to Noisy Big Data. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/0cr9523k

Not specified: Masters Thesis or Doctoral Dissertation