Joint Structured Graph Learning and Clustering Based on Concept Factorization

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

    Результат исследований: Глава в книге, отчете, сборнике статейМатериалы для конференциирецензирование

    5 Цитирования (Scopus)

    Аннотация

    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.

    Язык оригиналаАнглийский
    Название основной публикации2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
    ИздательInstitute of Electrical and Electronics Engineers Inc.
    Страницы3162-3166
    Число страниц5
    ISBN (электронное издание)9781479981311
    DOI
    СостояниеОпубликовано - мая 2019
    Событие44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, Великобритания
    Продолжительность: 12 мая 201917 мая 2019

    Серия публикаций

    НазваниеICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    Том2019-May
    ISSN (печатное издание)1520-6149

    Конференция

    Конференция44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
    Страна/TерриторияВеликобритания
    ГородBrighton
    Период12/05/1917/05/19

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