Flexible Non-negative Matrix Factorization with Adaptively Learned Graph Regularization

Yong Peng, Yanfang Long, Feiwei Qin, Wanzeng Kong, Feiping Nie, Andrzej Cichocki

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    7 Citations (Scopus)

    Abstract

    Non-negative matrix factorization (NMF) is an efficient model in learning parts-based data representation. Since the local geometrical structure can be effectively modeled by a nearest neighbor graph, the graph regularized NMF (GNMF) was proposed to make the learned representation more faithfully and better characterize the intrinsic structure of data. However, GNMF shares a similar paradigm with most of existing graph-based learning models which perform learning tasks on a fixed input graph. In this paper, we propose a new Flexible NMF model with adaptively learned Graph regularization (Γ NMΓ G) in which the graph is jointly learned with simultaneous performing the matrix factorization. An efficient iterative method with guaranteed convergence and relative low complexity is developed to optimize the FNMFG objective. Experiments compare FNMFG method with state-of-the-art algorithms and demonstrate its improved performance.

    Original languageEnglish
    Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3107-3111
    Number of pages5
    ISBN (Electronic)9781479981311
    DOIs
    Publication statusPublished - May 2019
    Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
    Duration: 12 May 201917 May 2019

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    Volume2019-May
    ISSN (Print)1520-6149

    Conference

    Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
    Country/TerritoryUnited Kingdom
    CityBrighton
    Period12/05/1917/05/19

    Keywords

    • adaptive graph learning
    • clustering
    • Non-negative matrix factorization

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