Learning the hierarchical parts of objects by deep non-smooth nonnegative matrix factorization

Jinshi Yu, Guoxu Zhou, Andrzej Cichocki, Shengli Xie

    Research output: Contribution to journalArticlepeer-review

    13 Citations (Scopus)


    Nonsmooth nonnegative matrix factorization (nsNMF) is capable of producing more localized, less overlapped feature representations than other variants of NMF while keeping satisfactory fit to data. However, nsNMF as well as other existing NMF methods are incompetent to learn hierarchical features of complex data due to its shallow structure. To fill this gap, we propose a deep nsNMF method coined by the fact that it possesses a deeper architecture compared with standard nsNMF. The deep nsNMF not only gives part-based features due to the nonnegativity constraints but also creates higher level, more abstract features by combing lower level ones. The in-depth description of how deep architecture can help to efficiently discover abstract features in dnsNMF is presented, suggesting that the proposed model inherits the major advantages from both deep learning and NMF. Extensive experiments demonstrate the standout performance of the proposed method in clustering analysis.

    Original languageEnglish
    Article number8481457
    Pages (from-to)58096-58105
    Number of pages10
    JournalIEEE Access
    Publication statusPublished - 2018


    • deep nsNMF
    • face clustering
    • features learning
    • Nonnegative matrix factorization (NMF)
    • nsNMF
    • sparseness


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