Fast Monte-Carlo algorithms for finding low-rank approximations

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Abstract
In several applications, the data consists of an m/spl times/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 according to a natural probability distribution. Indeed, in the applications such sampling is possible. Our main result is that we can find the description of a matrix D* of rank at most k so that /spl par/A-D*/spl par//sub F//spl les/min/D,rank(D)/spl les/k/spl par/A-D/spl par//sub F/+/spl epsiv//spl par/A/spl par//sub F/ holds with probability at least 1-/spl delta/. (For any matrix M, /spl par/M/spl par//sub F//sup 2/ denotes the sum of the squares of all the entries of M.) The algorithm takes time polynomial in k, 1//spl epsiv/, log(1//spl delta/) only, independent of m, n.

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