Low-rank matrix approximation with weights or missing data is NP-hard

Nicolas Gillis () and François Glineur
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Nicolas Gillis: Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium

No 2010075, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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).

Keywords: low-rank matrix approximation; weighted low-rank approximation; missing data; matrix completion with noise; PCA with missing data; computational complexity; maximum-edge biclique problem (search for similar items in EconPapers)
Date: 2010-11-01
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Working Paper: Low-rank matrix approximation with weights or missing data is NP-hard (2011)
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