2026-09-19T00:57:44Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1180932026-06-17T14:44:19Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Jonathan A. Kelner.
Musco, Christopher Paul
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-09-17T15:57:13Z
2018-09-17T15:57:13Z
2018
2018
http://hdl.handle.net/1721.1/118093
1052124098
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 189-208).
We study fast algorithms for linear algebraic problems that are ubiquitous in data analysis and machine learning. Examples include singular value decomposition and low-rank approximation, several varieties of linear regression, data clustering, and nonlinear kernel methods. To scale these problems to massive datasets, we design new algorithms based on random sampling and iterative refinement, tools that have become an essential part of modern computational linear algebra. We focus on methods that are provably accurate and efficient, while working well in practical applications. Open source code for many of the methods discussed in this thesis can be found at https://github.com/cpmusco.
by Christopher Paul Musco.
Ph.D.
208 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Faster linear algebra for data analysis and machine learning
Thesis
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