Low-Rank Matrix Approximation with Weights or Missing Data is NP-hard
Abstract
Weighted low-rank approximation (WLRA), a dimensionality reduction technique for data analysis, has been successfully used in several applications, such as in collaborative filtering to design recommender systems or in computer vision to recover structure from motion. In this paper, we study the computational complexity of WLRA and prove that it is NP-hard to find an approximate solution, even when a rank-one approximation is sought. Our proofs are based on a reduction from the maximum-edge biclique problem, and apply to strictly positive weights as well as binary weights (the latter corresponding to low-rank matrix approximation with missing data).
Cite
@article{arxiv.1012.0197,
title = {Low-Rank Matrix Approximation with Weights or Missing Data is NP-hard},
author = {Nicolas Gillis and François Glineur},
journal= {arXiv preprint arXiv:1012.0197},
year = {2012}
}
Comments
Proof of Lemma 4 (Lemma 3 in v1) has been corrected. Some remarks and comments have been added. Accepted in SIAM Journal on Matrix Analysis and Applications