Faster linear algebra for data analysis and machine learning

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Musco, Christopher Paul

Advisor dc:contributor.advisor

• Jonathan A. Kelner.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/118093

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/118093