2026-09-19T08:51:46Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/918102026-06-06T00:48:58Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Christopher Yu and Piotr Indyk.
Galvin, Timothy Matthew
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-11-24T18:36:19Z
2014-11-24T18:36:19Z
2014
2014
http://hdl.handle.net/1721.1/91810
894114940
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).
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.
by Timothy Matthew Galvin.
M.Eng.
55 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Faster streaming algorithms for low-rank matrix approximations
Thesis
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