Low-Rank Matrix Approximation with Weights or Missing Data Is NP-Hard
نویسندگان
چکیده
منابع مشابه
Low-Rank Matrix Approximation with Weights or Missing Data Is NP-Hard
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 prove that computing an optimal weighted low-rank approximation is NP-hard, already when a rank-one approximat...
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ژورنال
عنوان ژورنال: SIAM Journal on Matrix Analysis and Applications
سال: 2011
ISSN: 0895-4798,1095-7162
DOI: 10.1137/110820361