A fast and efficient algorithm for low-rank approximation of a matrix

  • Authors:

  • Nam H. Nguyen;Thong T. Do;Trac D. Tran

  • Affiliations:

  • Johns Hopkins University, Baltimore, MD, USA;Johns Hopkins University, Baltimore, MD, USA;Johns Hopkins University, Baltimore, MD, USA

  • Venue:
  • Proceedings of the forty-first annual ACM symposium on Theory of computing
  • Year:
  • 2009

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Abstract

The low-rank matrix approximation problem involves finding of a rank k version of a m x n matrix A, labeled Ak, such that Ak is as "close" as possible to the best SVD approximation version of A at the same rank level. Previous approaches approximate matrix A by non-uniformly adaptive sampling some columns (or rows) of A, hoping that this subset of columns contain enough information about A. The sub-matrix is then used for the approximation process. However, these approaches are often computationally intensive due to the complexity in the adaptive sampling. In this paper, we propose a fast and efficient algorithm which at first pre-processes matrix A in order to spread out information (energy) of every columns (or rows) of A, then randomly selects some of its columns (or rows). Finally, a rank-k approximation is generated from the row space of these selected sets. The preprocessing step is performed by uniformly randomizing signs of entries of A and transforming all columns of A by an orthonormal matrix F with existing fast implementation (e.g. Hadamard, FFT, DCT...). Our main contribution is summarized as follows. 1) We show that by uniformly selecting at random d rows of the preprocessed matrix with d = ( 1/η k max {log k, log 1/β} ), we guarantee the relative Frobenius norm error approximation: (1 + η) norm{A - Ak}F with probability at least 1 - 5β. 2) With d above, we establish a spectral norm error approximation: (2 + √2m/d) norm{A - Ak}2 with probability at least 1 - 2β. 3) The algorithm requires 2 passes over the data and runs in time (mn log d + (m+n) d2) which, as far as the best of our knowledge, is the fastest algorithm when the matrix A is dense. 4) As a bonus, applying this framework to the well-known least square approximation problem min norm{A x - b} where A ∈ Rm x r, we show that by randomly choosing d = (1/η γ r log m), the approximation solution is proportional to the optimal one with a factor of η and with extremely high probability, (1 - 6 m-γ), say.