Joint Structured Graph Learning and Clustering Based on Concept Factorization

Yong Peng, Rixin Tang, Wanzeng Kong, Jianhai Zhang, Feiping Nie, Andrzej Cichocki

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

    5 Citations (Scopus)

    Abstract

    As one of the matrix factorization models, concept factorization (CF) achieved promising performance in learning data representation in both original feature space and reproducible kernel Hilbert space (RKHS). Based on the consensuses that 1) learning performance of models can be enhanced by exploiting the geometrical structure of data and 2) jointly performing structured graph learning and clustering can avoid the suboptimal solutions caused by the two-stage strategy in graph-based learning, we developed a new CF model with self-expression. Our model has a combined coefficient matrix which is able to learn more efficiently. In other words, we propose a CF-based joint structured graph learning and clustering model (JSGCF). A new efficient iterative method is developed to optimize the JSGCF objective function. Experimental results on representative data sets demonstrate the effectiveness of our new JSGCF algorithm.

    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.
    Pages3162-3166
    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

    • clustering
    • concept factorization
    • joint learning
    • Structured graph learning

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