Faster linear algebra for data analysis and machine learning
Name
1052124098-MIT.pdf
Description
Full printable version
Size
46.47 MB
Format
Adobe PDF
Checksum (MD5)
ce794197a25eb46a3e18e9ef91a15e04
Author(s)
Musco, Christopher Paul
Advisor(s)
Jonathan A. Kelner.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
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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