Matrix Approximation under Local Low-Rank Assumption

Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer

Decision: conferencePoster-iclr2013-workshop

Abstract: Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is only locally of low-rank, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We analyze the accuracy of the proposed local low-rank modeling. Our experiments show improvements in prediction accuracy in recommendation tasks.