KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/660745
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AbstractMulti-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.
CitationWei, S., Wang, J., Yu, G., Domeniconi, C., & Zhang, X. (2020). Multi-View Multiple Clusterings Using Deep Matrix Factorization. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6348–6355. doi:10.1609/aaai.v34i04.6104
SponsorsThis 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.
Conference/Event name34th AAAI Conference on Artificial Intelligence, AAAI 2020