New Theory and Methods for Signals in Unions of Subspaces

Simple item page
dc.contributor.advisorBaraniuk, Richard G.en_US
dc.contributor.committeeMemberKoushanfar, Farinazen_US
dc.contributor.committeeMemberAllen, Geneveraen_US
dc.contributor.committeeMemberSabharwal, Ashutoshen_US
dc.creatorDyer, Eva Laurenen_US
dc.date.accessioned2017-05-11T22:06:29Zen_US
dc.date.available2017-05-11T22:06:29Zen_US
dc.date.created2014-05en_US
dc.date.issued2014-09-18en_US
dc.date.submittedMay 2014en_US
dc.date.updated2017-05-11T22:06:30Zen_US
dc.description.abstractThe rapid development and availability of cheap storage and sensing devices has quickly produced a deluge of high-dimensional data. While the dimensionality of modern datasets continues to grow, our saving grace is that these data often exhibit low-dimensional structure that can be exploited to compress, organize, and cluster massive collections of data. Signal models such as linear subspace models, remain one of the most widely used models for high-dimensional data; however, in many settings of interest, finding a global model that can capture all the relevant structure in the data is not possible. Thus, an alternative to learning a global model is to instead learn a hybrid model or a union of low-dimensional subspaces that model different subsets of signals in the dataset as living on distinct subspaces. This thesis develops new methods and theory for learning union of subspace models as well as exploiting multi-subspace structure in a wide range of signal processing and data analysis tasks. The main contributions of this thesis include new methods and theory for: (i) decomposing and subsampling datasets consisting of signals on unions of subspaces, (ii) subspace clustering for learning union of subspace models, and (iii) exploiting multi-subspace structure in order accelerate distributed computing and signal processing on massive collections of data. I demonstrate the utility of the proposed methods in a number of important imaging and computer vision applications including: illumination-invariant face recognition, segmentation of hyperspectral remote sensing data, and compression of video and lightfield data arising in 3D scene modeling and analysis.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationDyer, Eva Lauren. "New Theory and Methods for Signals in Unions of Subspaces." (2014) Diss., Rice University. <a href="https://hdl.handle.net/1911/94222">https://hdl.handle.net/1911/94222</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/94222en_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.subjectunion of subspacesen_US
dc.subjectsparse recoveryen_US
dc.subjectsubspace clusteringen_US
dc.titleNew Theory and Methods for Signals in Unions of Subspacesen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
DYER-DOCUMENT-2014.pdf
Size:
7.77 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
5.84 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
LICENSE.txt
Size:
2.6 KB
Format:
Plain Text
Description:
Download
Collections
Rice University Theses and Dissertations