Multi-view clustering aims to exploit information from data described by various views to optimize clustering performance. Multi-view clustering methods based on deep matrix factorization are capable of extracting intrinsic category information from data that contains a variety of knowledge. And the optimization process of deep matrix factorization suffers from several constraints. To address these issues, this paper proposes a multi-view clustering model via deep matrix factorization smoothed by local geometrical loss. Based on the assumption that samples close in original space are more likely to be in the same category, the local geometrical information is preserved during the training process of deep matrix factorization by maximizing the conditional distribution on the graphs built by different views. Then, inspired by the connection between deep auto-encoder and deep matrix factorization, we leverage a deep auto-encoder based solution to handle the optimization of the objective function. By adding an activation function to each layer of deep auto-encoders, the nonnegativity of features output from layers in deep matrix factorization is strictly guaranteed during the training process. Finally, we conduct extensive experiments on four datasets to validate that the proposed method is superior to state-of-the-arts.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Local Geometrical Deep Matrix Factorization for Multi-View Clustering


    Contributors:
    Lin, Pengfei (author) / Huang, Sheng (author) / Wang, Shiping (author)


    Publication date :

    2022-10-08


    Size :

    2296843 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Elevator equipment safety early warning method based on limited non-negative matrix factorization multi-view clustering

    XU HUANGZHEN / CHEN BENYAO / YU PING et al. | European Patent Office | 2023

    Free access


    Index Code Construction via Deep Matrix Factorization

    Vaisakh, M / Theagarajan, Lakshmi N. | IEEE | 2021


    A weighted non-negative matrix factorization for local representations

    Guillamet, D. / Bressan, M. / Vitria, J. | IEEE | 2001


    A Weighted Non-Negative Matrix Factorization for Local Representations

    Guillamet, D. / Bressan, M. / Vitria, J. et al. | British Library Conference Proceedings | 2001