Local Low-Rank Matrix Approximation
Venue
Proceedings of the 30th International Conference on Machine Learning (ICML), Journal of Machine Learning Research (2013)
Publication Year
2013
Authors
Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer
BibTeX
Abstract
Matrix approximation is a common tool in 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 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 over classical approaches for
recommendation tasks.
