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Two algorithms for orthogonal nonnegative matrix factorization with application to clusterin

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  • POMPILI, Filippo
  • GILLIS, Nicolas
  • ABSIL, Pierre-Antoine
  • GLINEUR, François

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Suggested Citation

  • POMPILI, Filippo & GILLIS, Nicolas & ABSIL, Pierre-Antoine & GLINEUR, François, 2014. "Two algorithms for orthogonal nonnegative matrix factorization with application to clusterin," LIDAM Reprints CORE 2581, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:2581
    Note: In : Neurocomputing, 141, 15-25, 2014
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    Cited by:

    1. Hiroyasu Abe & Hiroshi Yadohisa, 2019. "Orthogonal nonnegative matrix tri-factorization based on Tweedie distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 825-853, December.
    2. Ja’far Dehghanpour-Sahron & Nezam Mahdavi-Amiri, 2021. "A competitive optimization approach for data clustering and orthogonal non-negative matrix factorization," 4OR, Springer, vol. 19(4), pages 473-499, December.
    3. Masoud Ahookhosh & Le Thi Khanh Hien & Nicolas Gillis & Panagiotis Patrinos, 2021. "Multi-block Bregman proximal alternating linearized minimization and its application to orthogonal nonnegative matrix factorization," Computational Optimization and Applications, Springer, vol. 79(3), pages 681-715, July.

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