Faster streaming algorithms for low-rank matrix approximations
Name
894114940-MIT.pdf
Description
Full printable version
Size
3.49 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Galvin, Timothy Matthew
Advisor(s)
Christopher Yu and Piotr Indyk.
Date Issued
2014
Publisher
Massachusetts Institute of Technology
Abstract
Low-rank matrix approximations are used in a significant number of applications. We present new algorithms for generating such approximations in a streaming fashion that expand upon recently discovered matrix sketching techniques. We test our approaches on real and synthetic data to explore runtime and accuracy performance. We apply our algorithms to the technique of Latent Semantic Indexing on a widely studied data set. We find our algorithms provide strong empirical results.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 53-55).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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