TY - GEN
T1 - Multi-view multiple clusterings using deep matrix factorization
AU - Wei, Shaowei
AU - Wang, Jun
AU - Yu, Guoxian
AU - Domeniconi, Carlotta
AU - Zhang, Xiangliang
N1 - KAUST Repository Item: Exported on 2021-06-08
Acknowledgements: 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.
PY - 2020/4/3
Y1 - 2020/4/3
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/660745
UR - https://aaai.org/ojs/index.php/AAAI/article/view/6104
UR - http://www.scopus.com/inward/record.url?scp=85106566922&partnerID=8YFLogxK
U2 - 10.1609/aaai.v34i04.6104
DO - 10.1609/aaai.v34i04.6104
M3 - Conference contribution
SN - 9781577358350
SP - 6348
EP - 6355
BT - Proceedings of the AAAI Conference on Artificial Intelligence
PB - Association for the Advancement of Artificial Intelligence (AAAI)
ER -