2007/08/27 by Petros Drineas, Michael W. Mahoney, Drineas, Petros +3 · 8 citations
Computer Science · Mathematics · #Advanced Optimization Algorithms Research #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Matrix Theory and Algorithms #Polynomial and algebraic computation
paper · pdf · doi:10.48550/arxiv.0708.3696
openalex publication_date 2007/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many data analysis applications deal with large matrices and involve approximating the matrix using a small number of ``components.'' Typically, these components are linear combinations of the rows and columns of the matrix, and are thus difficult to interpret in terms of the original features of the input data. In this paper, we propose and study matrix approximations that are explicitly expressed in terms of a small number of columns and/or rows of the data matrix, and thereby more amenable to interpretation in terms of the original data. Our main algorithmic results are two randomized algorithms which take as input an m × n matrix A and a rank parameter k. In our first algorithm, C is chosen, and we let A'=CC+A, where C+ is the Moore-Penrose generalized inverse of C. In our second algorithm C, U, R are chosen, and we let A'=CUR. (C and R are matrices that consist of actual columns and rows, respectively, of A, and U is a generalized inverse of their intersection.) For each algorithm, we show that with probability at least 1-δ: ||A-A'||F ≤ (1+ε) ||A-Ak||F, where Ak is the ``best'' rank-k approximation provided by truncating the singular value decomposition (SVD) of A. The number of columns of C and rows of R is a low-degree polynomial in k, 1/ε, and log(1/δ). Our two algorithms are the first polynomial time algorithms for such low-rank matrix approximations that come with relative-error guarantees; previously, in some cases, it was not even known whether such matrix decompositions exist. Both of our algorithms are simple, they take time of the order needed to approximately compute the top k singular vectors of A, and they use a novel, intuitive sampling method called ``subspace sampling.''