Matrix completion via sparse factorization solved by accelerated proximal alternating linearized minimization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Original languageEnglish
Journal / PublicationIEEE TRANSACTIONS ON BIG DATA
Publication statusOnline published - 20 Sep 2018

Abstract

Classical matrix completion methods are not effective in recovering missing entries of data drawn from multiple subspaces because the matrices are often of high-rank. Recently a few advanced matrix completion methods were proposed to solve the problem but they are not scalable to large matrices and big data problems. This paper proposes a sparse factorization method for matrix completion on multiple-subspace data. The method factorizes the given incomplete matrix into a dense matrix and a sparse matrix, while the factorization errors of the observed entries are minimized. To solve the optimization problem, an accelerated proximal alternating linearized minimization (APALM) algorithm is proposed. As a non-trivial task owing to the alternation, linearization, nonconvexity, and extrapolation, the convergence of APALM is proved. APALM can solve a large class of optimization problems such as matrix factorization with nonsmooth regularizations. In addition, we show that, to recover an m × n matrix consisting of data drawn from k subspaces of dimension r0, the number of observed entries required in our matrix completion method is Ο(nr0 log k log n)while that in conventional methods is Ο(nr0k log n), which theoretically proves the superiority of our method on multiple-subspace data and high-rank matrices. The proposed matrix completion method is compared with state-of-the-art on synthetic data and real collaborative filtering problems. The experimental results corroborate that the proposed method can handle large matrices efficiently and provide high recovery accuracy.

Research Area(s)

  • matrix completion, sparse factorization, low-rank, accelerated proximal alternating linearized minimization, nonconvex optimization, collaborative filtering