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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," LIDAM Discussion Papers CORE 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

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    1. Belleflamme,Paul & Peitz,Martin, 2015. "Industrial Organization," Cambridge Books, Cambridge University Press, number 9781107687899, September.
    2. GILLIS, Nicolas & GLINEUR, François, 2008. "Nonnegative factorization and the maximum edge biclique problem," LIDAM Discussion Papers CORE 2008064, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    5. Peeters, M.J.P., 2003. "The maximum edge biclique problem is NP-complete," Other publications TiSEM 3e340431-37b3-4bc5-9b14-9, Tilburg University, School of Economics and Management.
    6. Rabah Amir, 2005. "Supermodularity and Complementarity in Economics: An Elementary Survey," Southern Economic Journal, John Wiley & Sons, vol. 71(3), pages 636-660, January.
    7. Markovsky, Ivan & Niranjan, Mahesan, 2010. "Approximate low-rank factorization with structured factors," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3411-3420, December.
    8. Huriot,Jean-Marie & Thisse,Jacques-François (ed.), 2009. "Economics of Cities," Cambridge Books, Cambridge University Press, number 9780521118279, January.
    9. Luc Bauwens & Winfried Pohlmeier & David Veredas (ed.), 2008. "High Frequency Financial Econometrics," Studies in Empirical Economics, Springer, number 978-3-7908-1992-2, January.
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    Cited by:

    1. Gillard, Jonathan & Usevich, Konstantin, 2018. "Structured low-rank matrix completion for forecasting in time series analysis," International Journal of Forecasting, Elsevier, vol. 34(4), pages 582-597.
    2. Nicolas Gillis & Stephen A. Vavasis, 2018. "On the Complexity of Robust PCA and ℓ 1 -Norm Low-Rank Matrix Approximation," Mathematics of Operations Research, INFORMS, vol. 43(4), pages 1072-1084, November.
    3. Glover, Fred & Ye, Tao & Punnen, Abraham P. & Kochenberger, Gary, 2015. "Integrating tabu search and VLSN search to develop enhanced algorithms: A case study using bipartite boolean quadratic programs," European Journal of Operational Research, Elsevier, vol. 241(3), pages 697-707.
    4. Qinghua Wu & Yang Wang & Fred Glover, 2020. "Advanced Tabu Search Algorithms for Bipartite Boolean Quadratic Programs Guided by Strategic Oscillation and Path Relinking," INFORMS Journal on Computing, INFORMS, vol. 32(1), pages 74-89, January.
    5. Namgil Lee & Jong-Min Kim, 2018. "Block tensor train decomposition for missing data estimation," Statistical Papers, Springer, vol. 59(4), pages 1283-1305, December.
    6. Zhikai Yang & Le Han, 2023. "A global exact penalty for rank-constrained optimization problem and applications," Computational Optimization and Applications, Springer, vol. 84(2), pages 477-508, March.

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