Endogenous Sparse Recovery

dc.contributor.advisorBaraniuk, Richard G.en_US
dc.creatorDyer, Eva L.en_US
dc.date.accessioned2013-03-08T00:33:32Zen_US
dc.date.available2013-03-08T00:33:32Zen_US
dc.date.issued2012en_US
dc.description.abstractSparsity has proven to be an essential ingredient in the development of efficient solutions to a number of problems in signal processing and machine learning. In all of these settings, sparse recovery methods are employed to recover signals that admit sparse representations in a pre-specified basis. Recently, sparse recovery methods have been employed in an entirely new way; instead of finding a sparse representation of a signal in a fixed basis, a sparse representation is formed "from within" the data. In this thesis, we study the utility of this endogenous sparse recovery procedure for learning unions of subspaces from collections of high-dimensional data. We provide new insights into the behavior of endogenous sparse recovery, develop sufficient conditions that describe when greedy methods will reveal local estimates of the subspaces in the ensemble, and introduce new methods to learn unions of overlapping subspaces from local subspace estimates.en_US
dc.format.extent82 p.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoTHESIS E.E. 2012 DYERen_US
dc.identifier.citationDyer, Eva L.. "Endogenous Sparse Recovery." (2012) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/70235">https://hdl.handle.net/1911/70235</a>.en_US
dc.identifier.digitalDyerEen_US
dc.identifier.urihttps://hdl.handle.net/1911/70235en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectApplied sciencesen_US
dc.subjectApplied mathematicsen_US
dc.subjectElectrical engineeringen_US
dc.titleEndogenous Sparse Recoveryen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
DyerE.pdf
Size:
5.18 MB
Format:
Adobe Portable Document Format