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

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  • GILLIS, Nicolas

    () (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium)

  • GLINEUR, François

    () (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium)

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).

Suggested Citation

  • GILLIS, Nicolas & GLINEUR, François, 2010. "Low-rank matrix approximation with weights or missing data is NP-hard," CORE Discussion Papers 2010075, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvco:2010075
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    References listed on IDEAS

    as
    1. GILLIS, Nicolas & GLINEUR, François, 2008. "Nonnegative factorization and the maximum edge biclique problem," CORE Discussion Papers 2008064, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Rabah Amir, 2005. "Supermodularity and Complementarity in Economics: An Elementary Survey," Southern Economic Journal, Southern Economic Association, vol. 71(3), pages 636-660, January.
    3. Winfried Pohlmeier & Luc Bauwens & David Veredas, 2007. "High frequency financial econometrics. Recent developments," ULB Institutional Repository 2013/136223, ULB -- Universite Libre de Bruxelles.
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