Faster linear algebra for data analysis and machine learning

• Main Author: Musco, Christopher Paul
• Other Authors: Jonathan A. Kelner.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2018
• Subjects: Electrical Engineering and Computer Science.
• Online Access: http://hdl.handle.net/1721.1/118093

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.