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You searched for subject:(Sparsity). Showing records 1 – 30 of 339 total matches.

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UCLA

1. Flynn, John Joseph. Learning a simplicial structure using sparsity.

Degree: Statistics, 2014, UCLA

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

APA (6th 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 (16th 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 (7th 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

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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

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

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APA (6th 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 (16th 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 (7th 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

 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

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

APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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.

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

MLA Handbook (7th 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.

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

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


University of Alberta

9. Bonar, Christopher David. Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data.

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

 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

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APA (6th 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 (16th 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 (7th 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

 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 (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Yang, Shenghao. “Split Cuts From Sparse Disjunctions.” 2019. Thesis, University of Waterloo. Accessed March 01, 2021. http://hdl.handle.net/10012/14363.

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

MLA Handbook (7th 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.

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

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

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

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APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

 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

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

APA (6th 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 (16th 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 (7th 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

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

APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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

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

APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

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

APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

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

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

APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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

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

APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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

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

APA (6th 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 (16th 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 (7th 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

 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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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

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

APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

[1] [2] [3] [4] [5] … [12]

.