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    Multi-View Multiple Clusterings using Deep Matrix Factorization

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    Type
    Preprint
    Authors
    Wei, Shaowei
    Wang, Jun
    Yu, Guoxian
    Carlotta,
    Zhang, Xiangliang cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2019-11-26
    Permanent link to this record
    http://hdl.handle.net/10754/660745
    
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    Abstract
    Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results. Existing multi-view clustering solutions can only output a single clustering of the data. Due to their multiplicity, multi-view data, can have different groupings that are reasonable and interesting from different perspectives. However, how to find multiple, meaningful, and diverse clustering results from multi-view data is still a rarely studied and challenging topic in multi-view clustering and multiple clusterings. In this paper, we introduce a deep matrix factorization based solution (DMClusts) to discover multiple clusterings. DMClusts gradually factorizes multi-view data matrices into representational subspaces layer-by-layer and generates one clustering in each layer. To enforce the diversity between generated clusterings, it minimizes a new redundancy quantification term derived from the proximity between samples in these subspaces. We further introduce an iterative optimization procedure to simultaneously seek multiple clusterings with quality and diversity. Experimental results on benchmark datasets confirm that DMClusts outperforms state-of-the-art multiple clustering solutions.
    Sponsors
    This work is supported by NSFC (61872300 and 61873214), Fundamental Research Funds for the Central Universities (XDJK2019B024), Natural Science Foundation of CQ CSTC (cstc2018jcyjAX0228) and by the King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
    Publisher
    arXiv
    arXiv
    1911.11396
    Additional Links
    https://arxiv.org/pdf/1911.11396
    Collections
    Preprints; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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