Dual Representation Learning for One-Step Clustering of Multi-View Data

• URL: http://arxiv.org/abs/2208.14450v1
• Date: Tue, 30 Aug 2022 14:20:26 GMT
• Title: Dual Representation Learning for One-Step Clustering of Multi-View Data
• Authors: Wei Zhang, Zhaohong Deng, Kup-Sze Choi, Jun Wang, Shitong Wang
• Abstract summary: We propose a novel one-step multi-view clustering method by exploiting the dual representation of both the common and specific information of different views. With this framework, the representation learning and clustering partition mutually benefit each other, which effectively improve the clustering performance.
• Score: 30.131568561100817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view data are commonly encountered in data mining applications. Effective extraction of information from multi-view data requires specific design of clustering methods to cater for data with multiple views, which is non-trivial and challenging. In this paper, we propose a novel one-step multi-view clustering method by exploiting the dual representation of both the common and specific information of different views. The motivation originates from the rationale that multi-view data contain not only the consistent knowledge between views but also the unique knowledge of each view. Meanwhile, to make the representation learning more specific to the clustering task, a one-step learning framework is proposed to integrate representation learning and clustering partition as a whole. With this framework, the representation learning and clustering partition mutually benefit each other, which effectively improve the clustering performance. Results from extensive experiments conducted on benchmark multi-view datasets clearly demonstrate the superiority of the proposed method.

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