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You searched for subject:(Dictionary Learning). Showing records 1 – 30 of 138 total matches.

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Rutgers University

1. Shakeri, Zahra, 1990-. Dictionary learning and multidimensional processing for tensor data.

Degree: PhD, Electrical and Computer Engineering, 2019, Rutgers University

 Modern machine learning and signal processing relies on finding meaningful and succinct representations of data. While most works in the literature have focused on finding… (more)

Subjects/Keywords: Dictionary learning; Machine learning

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APA (6th Edition):

Shakeri, Zahra, 1. (2019). Dictionary learning and multidimensional processing for tensor data. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/61955/

Chicago Manual of Style (16th Edition):

Shakeri, Zahra, 1990-. “Dictionary learning and multidimensional processing for tensor data.” 2019. Doctoral Dissertation, Rutgers University. Accessed December 04, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/61955/.

MLA Handbook (7th Edition):

Shakeri, Zahra, 1990-. “Dictionary learning and multidimensional processing for tensor data.” 2019. Web. 04 Dec 2020.

Vancouver:

Shakeri, Zahra 1. Dictionary learning and multidimensional processing for tensor data. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2020 Dec 04]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61955/.

Council of Science Editors:

Shakeri, Zahra 1. Dictionary learning and multidimensional processing for tensor data. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61955/


Penn State University

2. Bahrampour, Soheil. Sparse Representation for Information Fusion.

Degree: 2015, Penn State University

 Sparse representation methods have recently attracted much attention in the signal processing and machine learning community. The main underlying idea is that most of natural… (more)

Subjects/Keywords: Multimodal Dictionary Learning; Sparse Representation; Information Fusion

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APA (6th Edition):

Bahrampour, S. (2015). Sparse Representation for Information Fusion. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/24426

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):

Bahrampour, Soheil. “Sparse Representation for Information Fusion.” 2015. Thesis, Penn State University. Accessed December 04, 2020. https://submit-etda.libraries.psu.edu/catalog/24426.

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

MLA Handbook (7th Edition):

Bahrampour, Soheil. “Sparse Representation for Information Fusion.” 2015. Web. 04 Dec 2020.

Vancouver:

Bahrampour S. Sparse Representation for Information Fusion. [Internet] [Thesis]. Penn State University; 2015. [cited 2020 Dec 04]. Available from: https://submit-etda.libraries.psu.edu/catalog/24426.

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

Council of Science Editors:

Bahrampour S. Sparse Representation for Information Fusion. [Thesis]. Penn State University; 2015. Available from: https://submit-etda.libraries.psu.edu/catalog/24426

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

3. Pierre-Jean, Morgane. Development of statistical methods for DNA copy number analysis in cancerology : Développement de méthodes statistiques pour l'analyse du nombre de copies d'ADN en cancérologie.

Degree: Docteur es, Sciences de la vie et de la santé, 2016, Université Paris-Saclay (ComUE)

Les données génomiques issues d'expériences de puces à ADN ou de séquençage ont deux caractéristiques principales: leur grande dimension (le nombre de marqueurs dépassant de… (more)

Subjects/Keywords: Dictionary learning; Données génomiques à forte structure

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APA (6th Edition):

Pierre-Jean, M. (2016). Development of statistical methods for DNA copy number analysis in cancerology : Développement de méthodes statistiques pour l'analyse du nombre de copies d'ADN en cancérologie. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2016SACLE056

Chicago Manual of Style (16th Edition):

Pierre-Jean, Morgane. “Development of statistical methods for DNA copy number analysis in cancerology : Développement de méthodes statistiques pour l'analyse du nombre de copies d'ADN en cancérologie.” 2016. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed December 04, 2020. http://www.theses.fr/2016SACLE056.

MLA Handbook (7th Edition):

Pierre-Jean, Morgane. “Development of statistical methods for DNA copy number analysis in cancerology : Développement de méthodes statistiques pour l'analyse du nombre de copies d'ADN en cancérologie.” 2016. Web. 04 Dec 2020.

Vancouver:

Pierre-Jean M. Development of statistical methods for DNA copy number analysis in cancerology : Développement de méthodes statistiques pour l'analyse du nombre de copies d'ADN en cancérologie. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2016. [cited 2020 Dec 04]. Available from: http://www.theses.fr/2016SACLE056.

Council of Science Editors:

Pierre-Jean M. Development of statistical methods for DNA copy number analysis in cancerology : Développement de méthodes statistiques pour l'analyse du nombre de copies d'ADN en cancérologie. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2016. Available from: http://www.theses.fr/2016SACLE056


University of Wollongong

4. Yang, Jie. A machine learning paradigm based on sparse signal representation.

Degree: PhD, 2013, University of Wollongong

  Machine learning has been extensively investigated over the last three decades for its capability to learn mapping of functions from patterns. Nowadays, machine learning(more)

Subjects/Keywords: sparse signal representation; compressed sensing; machine learning; model selection; dictionary learning

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APA (6th Edition):

Yang, J. (2013). A machine learning paradigm based on sparse signal representation. (Doctoral Dissertation). University of Wollongong. Retrieved from 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, 0906 ELECTRICAL AND ELECTRONIC ENGINEERING ; https://ro.uow.edu.au/theses/3898

Chicago Manual of Style (16th Edition):

Yang, Jie. “A machine learning paradigm based on sparse signal representation.” 2013. Doctoral Dissertation, University of Wollongong. Accessed December 04, 2020. 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, 0906 ELECTRICAL AND ELECTRONIC ENGINEERING ; https://ro.uow.edu.au/theses/3898.

MLA Handbook (7th Edition):

Yang, Jie. “A machine learning paradigm based on sparse signal representation.” 2013. Web. 04 Dec 2020.

Vancouver:

Yang J. A machine learning paradigm based on sparse signal representation. [Internet] [Doctoral dissertation]. University of Wollongong; 2013. [cited 2020 Dec 04]. Available from: 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, 0906 ELECTRICAL AND ELECTRONIC ENGINEERING ; https://ro.uow.edu.au/theses/3898.

Council of Science Editors:

Yang J. A machine learning paradigm based on sparse signal representation. [Doctoral Dissertation]. University of Wollongong; 2013. Available from: 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, 0906 ELECTRICAL AND ELECTRONIC ENGINEERING ; https://ro.uow.edu.au/theses/3898


Carnegie Mellon University

5. Juefei-Xu, Felix. Unconstrained Periocular Face Recognition: From Reconstructive Dictionary Learning to Generative Deep Learning and Beyond.

Degree: 2018, Carnegie Mellon University

 Many real-world face recognition tasks are under unconstrained conditions such as off-angle pose variations, illumination variations, facial occlusion, facial expression, etc. In this work, we… (more)

Subjects/Keywords: Biometrics; Deep Learning; Dictionary Learning; Face Recognition; Periocular Recognition

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APA (6th Edition):

Juefei-Xu, F. (2018). Unconstrained Periocular Face Recognition: From Reconstructive Dictionary Learning to Generative Deep Learning and Beyond. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/1189

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):

Juefei-Xu, Felix. “Unconstrained Periocular Face Recognition: From Reconstructive Dictionary Learning to Generative Deep Learning and Beyond.” 2018. Thesis, Carnegie Mellon University. Accessed December 04, 2020. http://repository.cmu.edu/dissertations/1189.

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

MLA Handbook (7th Edition):

Juefei-Xu, Felix. “Unconstrained Periocular Face Recognition: From Reconstructive Dictionary Learning to Generative Deep Learning and Beyond.” 2018. Web. 04 Dec 2020.

Vancouver:

Juefei-Xu F. Unconstrained Periocular Face Recognition: From Reconstructive Dictionary Learning to Generative Deep Learning and Beyond. [Internet] [Thesis]. Carnegie Mellon University; 2018. [cited 2020 Dec 04]. Available from: http://repository.cmu.edu/dissertations/1189.

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

Council of Science Editors:

Juefei-Xu F. Unconstrained Periocular Face Recognition: From Reconstructive Dictionary Learning to Generative Deep Learning and Beyond. [Thesis]. Carnegie Mellon University; 2018. Available from: http://repository.cmu.edu/dissertations/1189

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


University of Toronto

6. Stolbunov, Valentin. Greedy Dictionary Learning Algorithms for Sparse Surrogate Modelling.

Degree: PhD, 2017, University of Toronto

 In the field of engineering design, numerical simulations are commonly used to forecast system performance before physical prototypes are built and tested. However the fidelity… (more)

Subjects/Keywords: Dictionary learning; Greedy algorithm; Machine learning; Regression; Supervised learning; Surrogate modelling; 0537

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APA (6th Edition):

Stolbunov, V. (2017). Greedy Dictionary Learning Algorithms for Sparse Surrogate Modelling. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/80697

Chicago Manual of Style (16th Edition):

Stolbunov, Valentin. “Greedy Dictionary Learning Algorithms for Sparse Surrogate Modelling.” 2017. Doctoral Dissertation, University of Toronto. Accessed December 04, 2020. http://hdl.handle.net/1807/80697.

MLA Handbook (7th Edition):

Stolbunov, Valentin. “Greedy Dictionary Learning Algorithms for Sparse Surrogate Modelling.” 2017. Web. 04 Dec 2020.

Vancouver:

Stolbunov V. Greedy Dictionary Learning Algorithms for Sparse Surrogate Modelling. [Internet] [Doctoral dissertation]. University of Toronto; 2017. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/1807/80697.

Council of Science Editors:

Stolbunov V. Greedy Dictionary Learning Algorithms for Sparse Surrogate Modelling. [Doctoral Dissertation]. University of Toronto; 2017. Available from: http://hdl.handle.net/1807/80697


Rochester Institute of Technology

7. Liu, Dengyu. Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging.

Degree: MS, Chester F. Carlson Center for Imaging Science (COS), 2015, Rochester Institute of Technology

  Cameras face a fundamental tradeoff between spatial and temporal resolution. Digital still cameras can capture images with high spatial resolution, but most high-speed video… (more)

Subjects/Keywords: Computational camera; Dictionary learning; Space-time sampling; Sparse reconstruction

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APA (6th Edition):

Liu, D. (2015). Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8906

Chicago Manual of Style (16th Edition):

Liu, Dengyu. “Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging.” 2015. Masters Thesis, Rochester Institute of Technology. Accessed December 04, 2020. https://scholarworks.rit.edu/theses/8906.

MLA Handbook (7th Edition):

Liu, Dengyu. “Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging.” 2015. Web. 04 Dec 2020.

Vancouver:

Liu D. Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging. [Internet] [Masters thesis]. Rochester Institute of Technology; 2015. [cited 2020 Dec 04]. Available from: https://scholarworks.rit.edu/theses/8906.

Council of Science Editors:

Liu D. Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging. [Masters Thesis]. Rochester Institute of Technology; 2015. Available from: https://scholarworks.rit.edu/theses/8906


University of Minnesota

8. Rambhatla, Sirisha. Semi-blind source separation via sparse representations and online dictionary learning.

Degree: 2012, University of Minnesota

University of Minnesota M.S. thesis. Major: Electrical Engineering. Advisor: Prof. Jarvis Haupt. 1 computer file (PDF); vii, 43 pages, appendix A.

This work examines a… (more)

Subjects/Keywords: Online dictionary learning; Semi-blind source separation; Sparse representations

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

APA (6th Edition):

Rambhatla, S. (2012). Semi-blind source separation via sparse representations and online dictionary learning. (Masters Thesis). University of Minnesota. Retrieved from http://purl.umn.edu/143883

Chicago Manual of Style (16th Edition):

Rambhatla, Sirisha. “Semi-blind source separation via sparse representations and online dictionary learning.” 2012. Masters Thesis, University of Minnesota. Accessed December 04, 2020. http://purl.umn.edu/143883.

MLA Handbook (7th Edition):

Rambhatla, Sirisha. “Semi-blind source separation via sparse representations and online dictionary learning.” 2012. Web. 04 Dec 2020.

Vancouver:

Rambhatla S. Semi-blind source separation via sparse representations and online dictionary learning. [Internet] [Masters thesis]. University of Minnesota; 2012. [cited 2020 Dec 04]. Available from: http://purl.umn.edu/143883.

Council of Science Editors:

Rambhatla S. Semi-blind source separation via sparse representations and online dictionary learning. [Masters Thesis]. University of Minnesota; 2012. Available from: http://purl.umn.edu/143883


Cornell University

9. Fang, Ruogu. Towards Robust Deconvolution In Medical Imaging: Informatics, Diagnosis And Treatment.

Degree: PhD, Electrical Engineering, 2014, Cornell University

Subjects/Keywords: Deconvolution; Dictionary Learning; Sparse Representation

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APA (6th Edition):

Fang, R. (2014). Towards Robust Deconvolution In Medical Imaging: Informatics, Diagnosis And Treatment. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/38911

Chicago Manual of Style (16th Edition):

Fang, Ruogu. “Towards Robust Deconvolution In Medical Imaging: Informatics, Diagnosis And Treatment.” 2014. Doctoral Dissertation, Cornell University. Accessed December 04, 2020. http://hdl.handle.net/1813/38911.

MLA Handbook (7th Edition):

Fang, Ruogu. “Towards Robust Deconvolution In Medical Imaging: Informatics, Diagnosis And Treatment.” 2014. Web. 04 Dec 2020.

Vancouver:

Fang R. Towards Robust Deconvolution In Medical Imaging: Informatics, Diagnosis And Treatment. [Internet] [Doctoral dissertation]. Cornell University; 2014. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/1813/38911.

Council of Science Editors:

Fang R. Towards Robust Deconvolution In Medical Imaging: Informatics, Diagnosis And Treatment. [Doctoral Dissertation]. Cornell University; 2014. Available from: http://hdl.handle.net/1813/38911


Penn State University

10. Vu, Tiep. Signal classification under structured sparsity constraints.

Degree: 2019, Penn State University

 Object Classification is a key direction of research in signal and image processing, computer vision and artificial intelligence. The goal is to come up with… (more)

Subjects/Keywords: Signal Classification; Sparsity Coding; Dictionary Learning; Image Classification; Optimization

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APA (6th Edition):

Vu, T. (2019). Signal classification under structured sparsity constraints. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/16112thv102

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):

Vu, Tiep. “Signal classification under structured sparsity constraints.” 2019. Thesis, Penn State University. Accessed December 04, 2020. https://submit-etda.libraries.psu.edu/catalog/16112thv102.

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

MLA Handbook (7th Edition):

Vu, Tiep. “Signal classification under structured sparsity constraints.” 2019. Web. 04 Dec 2020.

Vancouver:

Vu T. Signal classification under structured sparsity constraints. [Internet] [Thesis]. Penn State University; 2019. [cited 2020 Dec 04]. Available from: https://submit-etda.libraries.psu.edu/catalog/16112thv102.

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

Council of Science Editors:

Vu T. Signal classification under structured sparsity constraints. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/16112thv102

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


University of Houston

11. Chu, Dat 1985-. Discriminative Semi-coupled Dictionary Learning for Face Recognition.

Degree: MS, Computer Science, 2012, University of Houston

 Performing 3D face recognition when only partial 3D data are present in the gallery and probe is a very challenging task. The task is even… (more)

Subjects/Keywords: Face recognition; Sparse Respresentation; Dictionary learning; Computer science

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APA (6th Edition):

Chu, D. 1. (2012). Discriminative Semi-coupled Dictionary Learning for Face Recognition. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/827

Chicago Manual of Style (16th Edition):

Chu, Dat 1985-. “Discriminative Semi-coupled Dictionary Learning for Face Recognition.” 2012. Masters Thesis, University of Houston. Accessed December 04, 2020. http://hdl.handle.net/10657/827.

MLA Handbook (7th Edition):

Chu, Dat 1985-. “Discriminative Semi-coupled Dictionary Learning for Face Recognition.” 2012. Web. 04 Dec 2020.

Vancouver:

Chu D1. Discriminative Semi-coupled Dictionary Learning for Face Recognition. [Internet] [Masters thesis]. University of Houston; 2012. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/10657/827.

Council of Science Editors:

Chu D1. Discriminative Semi-coupled Dictionary Learning for Face Recognition. [Masters Thesis]. University of Houston; 2012. Available from: http://hdl.handle.net/10657/827

12. Adrielsson, Anders. Zedboard based platform for condition monitoring and control experiments.

Degree: Electrical and Space Engineering, 2018, Luleå University of Technology

  New methods for monitoring the condition of roller element bearings in rotating machinery offer possibilities to reduce repair- and maintenance costs, and reduced use… (more)

Subjects/Keywords: Condition Monitoring; Matching Pursuit; Dictionary Learning; Zedboard; Embedded Systems; Inbäddad systemteknik

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APA (6th Edition):

Adrielsson, A. (2018). Zedboard based platform for condition monitoring and control experiments. (Thesis). Luleå University of Technology. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-70105

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):

Adrielsson, Anders. “Zedboard based platform for condition monitoring and control experiments.” 2018. Thesis, Luleå University of Technology. Accessed December 04, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-70105.

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

MLA Handbook (7th Edition):

Adrielsson, Anders. “Zedboard based platform for condition monitoring and control experiments.” 2018. Web. 04 Dec 2020.

Vancouver:

Adrielsson A. Zedboard based platform for condition monitoring and control experiments. [Internet] [Thesis]. Luleå University of Technology; 2018. [cited 2020 Dec 04]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-70105.

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

Council of Science Editors:

Adrielsson A. Zedboard based platform for condition monitoring and control experiments. [Thesis]. Luleå University of Technology; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-70105

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


University of Minnesota

13. Rambhatla, Sirisha. Semi-blind source separation via sparse representations and online dictionary learning.

Degree: MS, Electrical Engineering, 2012, University of Minnesota

 This work examines a semi-blind source separation problem having applications in audio, image, and video processing. The essential aim is to separate one source whose… (more)

Subjects/Keywords: Online dictionary learning; Semi-blind source separation; Sparse representations

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

APA (6th Edition):

Rambhatla, S. (2012). Semi-blind source separation via sparse representations and online dictionary learning. (Masters Thesis). University of Minnesota. Retrieved from http://purl.umn.edu/143883

Chicago Manual of Style (16th Edition):

Rambhatla, Sirisha. “Semi-blind source separation via sparse representations and online dictionary learning.” 2012. Masters Thesis, University of Minnesota. Accessed December 04, 2020. http://purl.umn.edu/143883.

MLA Handbook (7th Edition):

Rambhatla, Sirisha. “Semi-blind source separation via sparse representations and online dictionary learning.” 2012. Web. 04 Dec 2020.

Vancouver:

Rambhatla S. Semi-blind source separation via sparse representations and online dictionary learning. [Internet] [Masters thesis]. University of Minnesota; 2012. [cited 2020 Dec 04]. Available from: http://purl.umn.edu/143883.

Council of Science Editors:

Rambhatla S. Semi-blind source separation via sparse representations and online dictionary learning. [Masters Thesis]. University of Minnesota; 2012. Available from: http://purl.umn.edu/143883

14. Geng, Quan. On the Local Correctness of L1-minimization for Dictionary Learning Algorithm.

Degree: MS, 1200, 2012, University of Illinois – Urbana-Champaign

 The idea that many classes of signals can be represented by linear combination of a small set of atoms of a dictionary has had a… (more)

Subjects/Keywords: Dictionary Learning; Compressed Sensing

…the dictionary learning problem is locally solvable. In Chapter 2, we describe the model in… …the dictionary learning problem can be solved, at least locally, via 1 minimization. The… …is the projection matrix onto Ωj . In dictionary learning problem, what we observe is… …has been proposed by Gribonval and Schnass [5] to solve the dictionary learning… …the dictionary learning problem is indeed solvable, not only locally. In Figure 2.1, we… 

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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APA (6th Edition):

Geng, Q. (2012). On the Local Correctness of L1-minimization for Dictionary Learning Algorithm. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/29431

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):

Geng, Quan. “On the Local Correctness of L1-minimization for Dictionary Learning Algorithm.” 2012. Thesis, University of Illinois – Urbana-Champaign. Accessed December 04, 2020. http://hdl.handle.net/2142/29431.

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

MLA Handbook (7th Edition):

Geng, Quan. “On the Local Correctness of L1-minimization for Dictionary Learning Algorithm.” 2012. Web. 04 Dec 2020.

Vancouver:

Geng Q. On the Local Correctness of L1-minimization for Dictionary Learning Algorithm. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2012. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/2142/29431.

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

Council of Science Editors:

Geng Q. On the Local Correctness of L1-minimization for Dictionary Learning Algorithm. [Thesis]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/29431

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


University of Tennessee – Knoxville

15. Luo, Jiajia. Feature Extraction and Recognition for Human Action Recognition.

Degree: 2014, University of Tennessee – Knoxville

 How to automatically label videos containing human motions is the task of human action recognition. Traditional human action recognition algorithms use the RGB videos as… (more)

Subjects/Keywords: feature extraction; feature representation; dictionary learning; sparse coding; Other Computer Engineering

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APA (6th Edition):

Luo, J. (2014). Feature Extraction and Recognition for Human Action Recognition. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/2710

Chicago Manual of Style (16th Edition):

Luo, Jiajia. “Feature Extraction and Recognition for Human Action Recognition.” 2014. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed December 04, 2020. https://trace.tennessee.edu/utk_graddiss/2710.

MLA Handbook (7th Edition):

Luo, Jiajia. “Feature Extraction and Recognition for Human Action Recognition.” 2014. Web. 04 Dec 2020.

Vancouver:

Luo J. Feature Extraction and Recognition for Human Action Recognition. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2014. [cited 2020 Dec 04]. Available from: https://trace.tennessee.edu/utk_graddiss/2710.

Council of Science Editors:

Luo J. Feature Extraction and Recognition for Human Action Recognition. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2014. Available from: https://trace.tennessee.edu/utk_graddiss/2710


University of Maryland

16. Chen, Yi-Chen. DISCRIMINATIVE LEARNING AND RECOGNITION USING DICTIONARIES.

Degree: Electrical Engineering, 2013, University of Maryland

 In recent years, the theory of sparse representation has emerged as a powerful tool for efficient processing of data in non-traditional ways. This is mainly… (more)

Subjects/Keywords: Electrical engineering; Computer science; Clustering; Dictionary; Learning; Recognition; Sparse Representation

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

APA (6th Edition):

Chen, Y. (2013). DISCRIMINATIVE LEARNING AND RECOGNITION USING DICTIONARIES. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/14573

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):

Chen, Yi-Chen. “DISCRIMINATIVE LEARNING AND RECOGNITION USING DICTIONARIES.” 2013. Thesis, University of Maryland. Accessed December 04, 2020. http://hdl.handle.net/1903/14573.

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

MLA Handbook (7th Edition):

Chen, Yi-Chen. “DISCRIMINATIVE LEARNING AND RECOGNITION USING DICTIONARIES.” 2013. Web. 04 Dec 2020.

Vancouver:

Chen Y. DISCRIMINATIVE LEARNING AND RECOGNITION USING DICTIONARIES. [Internet] [Thesis]. University of Maryland; 2013. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/1903/14573.

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

Council of Science Editors:

Chen Y. DISCRIMINATIVE LEARNING AND RECOGNITION USING DICTIONARIES. [Thesis]. University of Maryland; 2013. Available from: http://hdl.handle.net/1903/14573

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


University of Canterbury

17. Vafadar, Bahareh. Fast methods for Magnetic Resonance Angiography (MRA).

Degree: PhD, Electrical Engineering, 2014, University of Canterbury

 Magnetic resonance imaging (MRI) is a highly exible and non-invasive medical imaging modality based on the concept of nuclear magnetic resonance (NMR). Compared to other… (more)

Subjects/Keywords: Magnetic Resonance Angiography; Compresses Sensing; Image Reconstruction; Dictionary Learning

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

APA (6th Edition):

Vafadar, B. (2014). Fast methods for Magnetic Resonance Angiography (MRA). (Doctoral Dissertation). University of Canterbury. Retrieved from http://dx.doi.org/10.26021/3036

Chicago Manual of Style (16th Edition):

Vafadar, Bahareh. “Fast methods for Magnetic Resonance Angiography (MRA).” 2014. Doctoral Dissertation, University of Canterbury. Accessed December 04, 2020. http://dx.doi.org/10.26021/3036.

MLA Handbook (7th Edition):

Vafadar, Bahareh. “Fast methods for Magnetic Resonance Angiography (MRA).” 2014. Web. 04 Dec 2020.

Vancouver:

Vafadar B. Fast methods for Magnetic Resonance Angiography (MRA). [Internet] [Doctoral dissertation]. University of Canterbury; 2014. [cited 2020 Dec 04]. Available from: http://dx.doi.org/10.26021/3036.

Council of Science Editors:

Vafadar B. Fast methods for Magnetic Resonance Angiography (MRA). [Doctoral Dissertation]. University of Canterbury; 2014. Available from: http://dx.doi.org/10.26021/3036


University of Minnesota

18. Rambhalta, Sirisha. Provably Learning From Data: New Algorithms And Models For Matrix And Tensor Decompositions.

Degree: PhD, Electrical Engineering, 2019, University of Minnesota

Learning and leveraging patterns in data has fueled the recent advances in data driven services. As these solutions become more ubiquitous, and get incorporated into… (more)

Subjects/Keywords: Dictionary Learning; Hyperspectral imaging; Lidar; Matrix Decomposition; Robust PCA; Tensor Factorization

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APA (6th Edition):

Rambhalta, S. (2019). Provably Learning From Data: New Algorithms And Models For Matrix And Tensor Decompositions. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/215072

Chicago Manual of Style (16th Edition):

Rambhalta, Sirisha. “Provably Learning From Data: New Algorithms And Models For Matrix And Tensor Decompositions.” 2019. Doctoral Dissertation, University of Minnesota. Accessed December 04, 2020. http://hdl.handle.net/11299/215072.

MLA Handbook (7th Edition):

Rambhalta, Sirisha. “Provably Learning From Data: New Algorithms And Models For Matrix And Tensor Decompositions.” 2019. Web. 04 Dec 2020.

Vancouver:

Rambhalta S. Provably Learning From Data: New Algorithms And Models For Matrix And Tensor Decompositions. [Internet] [Doctoral dissertation]. University of Minnesota; 2019. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/11299/215072.

Council of Science Editors:

Rambhalta S. Provably Learning From Data: New Algorithms And Models For Matrix And Tensor Decompositions. [Doctoral Dissertation]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/215072


Utah State University

19. Pound, Andrew E. Exploiting Sparsity and Dictionary Learning to Efficiently Classify Materials in Hyperspectral Imagery.

Degree: MS, Electrical and Computer Engineering, 2014, Utah State University

  Hyperspectral imaging (HSI) produces spatial images with pixels that, instead of consisting of three colors, consist of hundreds of spectral measurements. Because there are… (more)

Subjects/Keywords: Exploiting Sparsity; Dictionary Learning; Hyperspectral Imagery; Electrical and Computer Engineering

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

APA (6th Edition):

Pound, A. E. (2014). Exploiting Sparsity and Dictionary Learning to Efficiently Classify Materials in Hyperspectral Imagery. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/4020

Chicago Manual of Style (16th Edition):

Pound, Andrew E. “Exploiting Sparsity and Dictionary Learning to Efficiently Classify Materials in Hyperspectral Imagery.” 2014. Masters Thesis, Utah State University. Accessed December 04, 2020. https://digitalcommons.usu.edu/etd/4020.

MLA Handbook (7th Edition):

Pound, Andrew E. “Exploiting Sparsity and Dictionary Learning to Efficiently Classify Materials in Hyperspectral Imagery.” 2014. Web. 04 Dec 2020.

Vancouver:

Pound AE. Exploiting Sparsity and Dictionary Learning to Efficiently Classify Materials in Hyperspectral Imagery. [Internet] [Masters thesis]. Utah State University; 2014. [cited 2020 Dec 04]. Available from: https://digitalcommons.usu.edu/etd/4020.

Council of Science Editors:

Pound AE. Exploiting Sparsity and Dictionary Learning to Efficiently Classify Materials in Hyperspectral Imagery. [Masters Thesis]. Utah State University; 2014. Available from: https://digitalcommons.usu.edu/etd/4020

20. Chadjipapa, Elisavet - Eleni. Διερεύνηση στρατηγικών χρήσης λεξικού μαθητών Δημοτικού και Γυμνασίου.

Degree: 2018, Democritus University of Thrace (DUTH); Δημοκρίτειο Πανεπιστήμιο Θράκης (ΔΠΘ)

According to Korais ‘The first book of every nation is the dictionary’ and thus he portrays it through the eyes of a lexicographer. This image… (more)

Subjects/Keywords: Χρήση λεξικού; Γλωσσικές στρατηγικές μάθησης; Στρατηγικές χρήσης λεξικού; Στρατηγική χρήση του λεξικού; Dictionary use; Language learning strategies; Dictionary Use Strategies; Strategic dictionary use

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

APA (6th Edition):

Chadjipapa, E. -. E. (2018). Διερεύνηση στρατηγικών χρήσης λεξικού μαθητών Δημοτικού και Γυμνασίου. (Thesis). Democritus University of Thrace (DUTH); Δημοκρίτειο Πανεπιστήμιο Θράκης (ΔΠΘ). Retrieved from http://hdl.handle.net/10442/hedi/43947

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):

Chadjipapa, Elisavet - Eleni. “Διερεύνηση στρατηγικών χρήσης λεξικού μαθητών Δημοτικού και Γυμνασίου.” 2018. Thesis, Democritus University of Thrace (DUTH); Δημοκρίτειο Πανεπιστήμιο Θράκης (ΔΠΘ). Accessed December 04, 2020. http://hdl.handle.net/10442/hedi/43947.

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

MLA Handbook (7th Edition):

Chadjipapa, Elisavet - Eleni. “Διερεύνηση στρατηγικών χρήσης λεξικού μαθητών Δημοτικού και Γυμνασίου.” 2018. Web. 04 Dec 2020.

Vancouver:

Chadjipapa E-E. Διερεύνηση στρατηγικών χρήσης λεξικού μαθητών Δημοτικού και Γυμνασίου. [Internet] [Thesis]. Democritus University of Thrace (DUTH); Δημοκρίτειο Πανεπιστήμιο Θράκης (ΔΠΘ); 2018. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/10442/hedi/43947.

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

Council of Science Editors:

Chadjipapa E-E. Διερεύνηση στρατηγικών χρήσης λεξικού μαθητών Δημοτικού και Γυμνασίου. [Thesis]. Democritus University of Thrace (DUTH); Δημοκρίτειο Πανεπιστήμιο Θράκης (ΔΠΘ); 2018. Available from: http://hdl.handle.net/10442/hedi/43947

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


Georgia Tech

21. Mehta, Nishant A. On sparse representations and new meta-learning paradigms for representation learning.

Degree: PhD, Computer Science, 2013, Georgia Tech

 Given the "right" representation, learning is easy. This thesis studies representation learning and meta-learning, with a special focus on sparse representations. Meta-learning is fundamental to… (more)

Subjects/Keywords: Learning theory; Data-dependent complexity; Luckiness; Dictionary learning; Sparse coding; Lasso; Multi-task learning; Meta-learning; Learning to learn

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

APA (6th Edition):

Mehta, N. A. (2013). On sparse representations and new meta-learning paradigms for representation learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52159

Chicago Manual of Style (16th Edition):

Mehta, Nishant A. “On sparse representations and new meta-learning paradigms for representation learning.” 2013. Doctoral Dissertation, Georgia Tech. Accessed December 04, 2020. http://hdl.handle.net/1853/52159.

MLA Handbook (7th Edition):

Mehta, Nishant A. “On sparse representations and new meta-learning paradigms for representation learning.” 2013. Web. 04 Dec 2020.

Vancouver:

Mehta NA. On sparse representations and new meta-learning paradigms for representation learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/1853/52159.

Council of Science Editors:

Mehta NA. On sparse representations and new meta-learning paradigms for representation learning. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/52159


University of Rochester

22. Shafipour, Rasoul. Learning representations for signal and data processing on directed graphs.

Degree: PhD, 2020, University of Rochester

 Network processes are becoming increasingly ubiquitous, with examples ranging from the measurements of neural activities at different regions of the brain to infectious states of… (more)

Subjects/Keywords: Dictionary learning; Distributed data processing; Graph Fourier transform; Graph learning; Graph signal processing; Online optimization

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APA (6th Edition):

Shafipour, R. (2020). Learning representations for signal and data processing on directed graphs. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/35751

Chicago Manual of Style (16th Edition):

Shafipour, Rasoul. “Learning representations for signal and data processing on directed graphs.” 2020. Doctoral Dissertation, University of Rochester. Accessed December 04, 2020. http://hdl.handle.net/1802/35751.

MLA Handbook (7th Edition):

Shafipour, Rasoul. “Learning representations for signal and data processing on directed graphs.” 2020. Web. 04 Dec 2020.

Vancouver:

Shafipour R. Learning representations for signal and data processing on directed graphs. [Internet] [Doctoral dissertation]. University of Rochester; 2020. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/1802/35751.

Council of Science Editors:

Shafipour R. Learning representations for signal and data processing on directed graphs. [Doctoral Dissertation]. University of Rochester; 2020. Available from: http://hdl.handle.net/1802/35751


University of Michigan

23. Moore, Brian. Robust Algorithms for Low-Rank and Sparse Matrix Models.

Degree: PhD, Electrical Engineering: Systems, 2018, University of Michigan

 Data in statistical signal processing problems is often inherently matrix-valued, and a natural first step in working with such data is to impose a model… (more)

Subjects/Keywords: machine learning; signal processing; optimization; statistics; robust algorithms; dictionary learning; Computer Science; Electrical Engineering; Engineering

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

APA (6th Edition):

Moore, B. (2018). Robust Algorithms for Low-Rank and Sparse Matrix Models. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/143925

Chicago Manual of Style (16th Edition):

Moore, Brian. “Robust Algorithms for Low-Rank and Sparse Matrix Models.” 2018. Doctoral Dissertation, University of Michigan. Accessed December 04, 2020. http://hdl.handle.net/2027.42/143925.

MLA Handbook (7th Edition):

Moore, Brian. “Robust Algorithms for Low-Rank and Sparse Matrix Models.” 2018. Web. 04 Dec 2020.

Vancouver:

Moore B. Robust Algorithms for Low-Rank and Sparse Matrix Models. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/2027.42/143925.

Council of Science Editors:

Moore B. Robust Algorithms for Low-Rank and Sparse Matrix Models. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/143925


Duke University

24. Li, Lingbo. Nonparametric Bayesian Models for Joint Analysis of Imagery and Text .

Degree: 2014, Duke University

  It has been increasingly important to develop statistical models to manage large-scale high-dimensional image data. This thesis presents novel hierarchical nonparametric Bayesian models for… (more)

Subjects/Keywords: Electrical engineering; Statistics; Computer science; Bayesian Nonparametrics; Dictionary Learning; Image Processing; Machine Learning; Topic Modeling

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

APA (6th Edition):

Li, L. (2014). Nonparametric Bayesian Models for Joint Analysis of Imagery and Text . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/8675

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

Chicago Manual of Style (16th Edition):

Li, Lingbo. “Nonparametric Bayesian Models for Joint Analysis of Imagery and Text .” 2014. Thesis, Duke University. Accessed December 04, 2020. http://hdl.handle.net/10161/8675.

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

MLA Handbook (7th Edition):

Li, Lingbo. “Nonparametric Bayesian Models for Joint Analysis of Imagery and Text .” 2014. Web. 04 Dec 2020.

Vancouver:

Li L. Nonparametric Bayesian Models for Joint Analysis of Imagery and Text . [Internet] [Thesis]. Duke University; 2014. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/10161/8675.

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

Council of Science Editors:

Li L. Nonparametric Bayesian Models for Joint Analysis of Imagery and Text . [Thesis]. Duke University; 2014. Available from: http://hdl.handle.net/10161/8675

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


University of Maryland

25. Ni, Jie. Restoration and Domain Adaptation for Unconstrained Face Recognition.

Degree: Electrical Engineering, 2014, University of Maryland

 Face recognition (FR) has received great attention and tremendous progress has been made during the past two decades. While FR at close range under controlled… (more)

Subjects/Keywords: Electrical engineering; Dictionary learning; Domain adaptation; Face recognition; Image deconvolution; Manifold learning

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APA (6th Edition):

Ni, J. (2014). Restoration and Domain Adaptation for Unconstrained Face Recognition. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/16414

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):

Ni, Jie. “Restoration and Domain Adaptation for Unconstrained Face Recognition.” 2014. Thesis, University of Maryland. Accessed December 04, 2020. http://hdl.handle.net/1903/16414.

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

MLA Handbook (7th Edition):

Ni, Jie. “Restoration and Domain Adaptation for Unconstrained Face Recognition.” 2014. Web. 04 Dec 2020.

Vancouver:

Ni J. Restoration and Domain Adaptation for Unconstrained Face Recognition. [Internet] [Thesis]. University of Maryland; 2014. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/1903/16414.

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

Council of Science Editors:

Ni J. Restoration and Domain Adaptation for Unconstrained Face Recognition. [Thesis]. University of Maryland; 2014. Available from: http://hdl.handle.net/1903/16414

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


University of Sydney

26. Zhang, Tong. An integrated framework for magnetic resonance image reconstruction and segmentation .

Degree: 2016, University of Sydney

 As a non-invasive tomographic imaging technology, magnetic resonance imaging (MRI) has revolutionized the diagnostic imaging industry. Due to its relatively high resolution and superior soft-tissue… (more)

Subjects/Keywords: magnetic resonance (MR); image reconstruction; image segmentation; dictionary learning; kernel learning; Markov random field

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APA (6th Edition):

Zhang, T. (2016). An integrated framework for magnetic resonance image reconstruction and segmentation . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/15457

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):

Zhang, Tong. “An integrated framework for magnetic resonance image reconstruction and segmentation .” 2016. Thesis, University of Sydney. Accessed December 04, 2020. http://hdl.handle.net/2123/15457.

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

MLA Handbook (7th Edition):

Zhang, Tong. “An integrated framework for magnetic resonance image reconstruction and segmentation .” 2016. Web. 04 Dec 2020.

Vancouver:

Zhang T. An integrated framework for magnetic resonance image reconstruction and segmentation . [Internet] [Thesis]. University of Sydney; 2016. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/2123/15457.

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

Council of Science Editors:

Zhang T. An integrated framework for magnetic resonance image reconstruction and segmentation . [Thesis]. University of Sydney; 2016. Available from: http://hdl.handle.net/2123/15457

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

27. Liu, Yuan. Représentation parcimonieuse basée sur la norme ℓ₀ : ℓ₀ based sparse representation.

Degree: Docteur es, Informatique, 2019, Normandie

Cette monographie traite du problème d’apprentissage de dictionnaire parcimonieux associé à la pseudo-norme ℓ₀. Ce problème est classiquement traité par une procédure de relaxation alternée… (more)

Subjects/Keywords: Apprentissage de dictionnaire; Codage parcimonieux; Apprentissage de dictionnaire à faible cohérence; Lagrangien augmenté; PALM; Sparse representation; Sparse coding; Dictionary learning; MIQP; Incoherent dictionary learning; Augmented Lagrangian method; PALM

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

APA (6th Edition):

Liu, Y. (2019). Représentation parcimonieuse basée sur la norme ℓ₀ : ℓ₀ based sparse representation. (Doctoral Dissertation). Normandie. Retrieved from http://www.theses.fr/2019NORMIR22

Chicago Manual of Style (16th Edition):

Liu, Yuan. “Représentation parcimonieuse basée sur la norme ℓ₀ : ℓ₀ based sparse representation.” 2019. Doctoral Dissertation, Normandie. Accessed December 04, 2020. http://www.theses.fr/2019NORMIR22.

MLA Handbook (7th Edition):

Liu, Yuan. “Représentation parcimonieuse basée sur la norme ℓ₀ : ℓ₀ based sparse representation.” 2019. Web. 04 Dec 2020.

Vancouver:

Liu Y. Représentation parcimonieuse basée sur la norme ℓ₀ : ℓ₀ based sparse representation. [Internet] [Doctoral dissertation]. Normandie; 2019. [cited 2020 Dec 04]. Available from: http://www.theses.fr/2019NORMIR22.

Council of Science Editors:

Liu Y. Représentation parcimonieuse basée sur la norme ℓ₀ : ℓ₀ based sparse representation. [Doctoral Dissertation]. Normandie; 2019. Available from: http://www.theses.fr/2019NORMIR22


NSYSU

28. Chiou, Yi-Wen. Image/Video Deblocking via Sparse Representation.

Degree: Master, Electrical Engineering, 2012, NSYSU

 Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a common problem in block-based image/video compression, especially at low bitrate… (more)

Subjects/Keywords: dictionary learning; MCA (morphological component analysis); sparse representation; Blocking artifact; sparse coding

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

APA (6th Edition):

Chiou, Y. (2012). Image/Video Deblocking via Sparse Representation. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0908112-231320

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):

Chiou, Yi-Wen. “Image/Video Deblocking via Sparse Representation.” 2012. Thesis, NSYSU. Accessed December 04, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0908112-231320.

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

MLA Handbook (7th Edition):

Chiou, Yi-Wen. “Image/Video Deblocking via Sparse Representation.” 2012. Web. 04 Dec 2020.

Vancouver:

Chiou Y. Image/Video Deblocking via Sparse Representation. [Internet] [Thesis]. NSYSU; 2012. [cited 2020 Dec 04]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0908112-231320.

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

Council of Science Editors:

Chiou Y. Image/Video Deblocking via Sparse Representation. [Thesis]. NSYSU; 2012. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0908112-231320

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


Universidade do Rio Grande do Sul

29. Flores, Eliezer Soares. Segmentação de lesões melanocíticas usando uma abordagem baseada no aprendizado de dicionários.

Degree: 2015, Universidade do Rio Grande do Sul

Segmentação é uma etapa essencial para sistemas de pré-triagem de lesões melanocíticas. Neste trabalho, um novo método para segmentar lesões melanocíticas em imagens de câmera… (more)

Subjects/Keywords: Computação gráfica; Segmentation; Melanocytic lesions; Processamento : Imagem; Informática médica; Macroscopic images; Dictionary learning

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APA (6th Edition):

Flores, E. S. (2015). Segmentação de lesões melanocíticas usando uma abordagem baseada no aprendizado de dicionários. (Thesis). Universidade do Rio Grande do Sul. Retrieved from http://hdl.handle.net/10183/115219

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):

Flores, Eliezer Soares. “Segmentação de lesões melanocíticas usando uma abordagem baseada no aprendizado de dicionários.” 2015. Thesis, Universidade do Rio Grande do Sul. Accessed December 04, 2020. http://hdl.handle.net/10183/115219.

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

MLA Handbook (7th Edition):

Flores, Eliezer Soares. “Segmentação de lesões melanocíticas usando uma abordagem baseada no aprendizado de dicionários.” 2015. Web. 04 Dec 2020.

Vancouver:

Flores ES. Segmentação de lesões melanocíticas usando uma abordagem baseada no aprendizado de dicionários. [Internet] [Thesis]. Universidade do Rio Grande do Sul; 2015. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/10183/115219.

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

Council of Science Editors:

Flores ES. Segmentação de lesões melanocíticas usando uma abordagem baseada no aprendizado de dicionários. [Thesis]. Universidade do Rio Grande do Sul; 2015. Available from: http://hdl.handle.net/10183/115219

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


Colorado State University

30. Kopacz, Justin. Optimal dictionary learning with application to underwater target detection from synthetic aperture sonar imagery.

Degree: MS(M.S.), Electrical and Computer Engineering, 2014, Colorado State University

 K-SVD is a relatively new method used to create a dictionary matrix that best ts a set of training data vectors formed with the intent… (more)

Subjects/Keywords: dictionary learning; underwater target detection; sparse coding; orthogonal matching pursuit; K-SVD

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APA (6th Edition):

Kopacz, J. (2014). Optimal dictionary learning with application to underwater target detection from synthetic aperture sonar imagery. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/82647

Chicago Manual of Style (16th Edition):

Kopacz, Justin. “Optimal dictionary learning with application to underwater target detection from synthetic aperture sonar imagery.” 2014. Masters Thesis, Colorado State University. Accessed December 04, 2020. http://hdl.handle.net/10217/82647.

MLA Handbook (7th Edition):

Kopacz, Justin. “Optimal dictionary learning with application to underwater target detection from synthetic aperture sonar imagery.” 2014. Web. 04 Dec 2020.

Vancouver:

Kopacz J. Optimal dictionary learning with application to underwater target detection from synthetic aperture sonar imagery. [Internet] [Masters thesis]. Colorado State University; 2014. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/10217/82647.

Council of Science Editors:

Kopacz J. Optimal dictionary learning with application to underwater target detection from synthetic aperture sonar imagery. [Masters Thesis]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/82647

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