Matrix sampling algorithms for topics in machine learning
Authors
Paul, Saurabh
ORCID
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Other Contributors
Drineas, Petros
Magdon-Ismail, Malik
Zaki, Mohammed J., 1971-
Bennett, Kristin P.
Magdon-Ismail, Malik
Zaki, Mohammed J., 1971-
Bennett, Kristin P.
Issue Date
2015-05
Keywords
Computer science
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
4. Adaptive Sampling algorithm for matrix reconstruction: We introduce a new adaptive sampling algorithm for computing low-rank matrix approximation. We are given a matrix A and a target rank k. The algorithm runs in t iterations and selects a subset of columns of the matrix. It computes a rank-k approximation to the matrix that is as good as the best rank-k approximation that would have been obtained by using all the columns.
Description
May 2015
School of Science
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
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