jmlr jmlr2012 jmlr2012-43 jmlr2012-43-reference knowledge-graph by maker-knowledge-mining
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Author: Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, David P. Woodruff
Abstract: The statistical leverage scores of a matrix A are the squared row-norms of the matrix containing its (top) left singular vectors and the coherence is the largest leverage score. These quantities are of interest in recently-popular problems such as matrix completion and Nystr¨ m-based low-rank o matrix approximation as well as in large-scale statistical data analysis applications more generally; moreover, they are of interest since they define the key structural nonuniformity that must be dealt with in developing fast randomized matrix algorithms. Our main result is a randomized algorithm that takes as input an arbitrary n × d matrix A, with n ≫ d, and that returns as output relative-error approximations to all n of the statistical leverage scores. The proposed algorithm runs (under assumptions on the precise values of n and d) in O(nd log n) time, as opposed to the O(nd 2 ) time required by the na¨ve algorithm that involves computing an orthogonal basis for the ı range of A. Our analysis may be viewed in terms of computing a relative-error approximation to an underconstrained least-squares approximation problem, or, relatedly, it may be viewed as an application of Johnson-Lindenstrauss type ideas. Several practically-important extensions of our basic result are also described, including the approximation of so-called cross-leverage scores, the extension of these ideas to matrices with n ≈ d, and the extension to streaming environments. Keywords: matrix coherence, statistical leverage, randomized algorithm
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