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Abstract: In several applications, the data consists of an m \Theta n matrix A and it is of interest to find an approximation D of a specified rank k to A where, k is much smaller than m and n. Traditional methods like the Singular Value Decomposition (SVD) help us find the "best" such approximation. However, these methods take time polynomial in m;n which is often too prohibitive. In this paper, we develop an algorithm which is qualitatively faster provided we may sample the entries of the matrix... (Update)
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BibTeX entry: (Update)
A. Frieze, R. Kannan and S. Vempala, "Fast Monte-Carlo Algorithms for finding low-rank approximations," Proc. 1998 FOCS, pp. 370-378, 1998. http://citeseer.ist.psu.edu/article/frieze98fast.html More
@inproceedings{ frieze98fast,
author = "Alan M. Frieze and Ravi Kannan and Santosh Vempala",
title = "Fast Monte-Carlo Algorithms for Finding Low-Rank Approximations",
booktitle = "{IEEE} Symposium on Foundations of Computer Science",
pages = "370-378",
year = "1998",
url = "citeseer.ist.psu.edu/article/frieze98fast.html" }
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