LLORMA: Local Low-Rank Matrix Approximation
Venue
Journal of Machine Learning Research (JMLR), vol. 17 (2016), pp. 1-24
Publication Year
2016
Authors
Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer, Samy Bengio
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 low-rank. In this paper, we propose, analyze,
and experiment with two procedures, one parallel and the other global, for
constructing local matrix approximations. The two approaches approximate the
observed matrix as a weighted sum of low-rank matrices. These matrices are limited
to a local region of the observed matrix. We analyze the accuracy of the proposed
local low-rank modeling. Our experiments show improvements in prediction accuracy
over classical approaches for recommendation tasks.
