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dc.contributor.advisor Zhang, Yin
dc.contributor.advisor Yin, Wotao
dc.creatorDeng, Wei
dc.date.accessioned 2012-09-06T04:29:23Z
dc.date.accessioned 2012-09-06T04:29:25Z
dc.date.available 2012-09-06T04:29:23Z
dc.date.available 2012-09-06T04:29:25Z
dc.date.created 2012-05
dc.date.issued 2012-09-05
dc.date.submitted May 2012
dc.identifier.urihttps://hdl.handle.net/1911/64676
dc.description.abstract Group sparsity reveals underlying sparsity patterns and contains rich structural information in data. Hence, exploiting group sparsity will facilitate more efficient techniques for recovering large and complicated data in applications such as compressive sensing, statistics, signal and image processing, machine learning and computer vision. This thesis develops efficient algorithms for solving a class of optimization problems with group sparse solutions, where arbitrary group configurations are allowed and the mixed L21-regularization is used to promote group sparsity. Such optimization problems can be quite challenging to solve due to the mixed-norm structure and possible grouping irregularities. We derive algorithms based on a variable splitting strategy and the alternating direction methodology. Extensive numerical results are presented to demonstrate the efficiency, stability and robustness of these algorithms, in comparison with the previously known state-of-the-art algorithms. We also extend the existing global convergence theory to allow more generality.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectGroup sparsity
Alternating direction method
Augmented Lagrangian
Compressive sensing
Group lasso
Joint sparsity
dc.title Recovering Data with Group Sparsity by Alternating Direction Methods
dc.contributor.committeeMember Baraniuk, Richard G.
dc.date.updated 2012-09-06T04:29:25Z
dc.identifier.slug 123456789/ETD-2012-05-141
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Computational and Applied Mathematics
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Masters
thesis.degree.name Master of Arts
dc.identifier.citation Deng, Wei. "Recovering Data with Group Sparsity by Alternating Direction Methods." (2012) Master’s Thesis, Rice University. https://hdl.handle.net/1911/64676.


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