Incomplete Multi-view Clustering via Cross-view Relation Transfer

• URL: http://arxiv.org/abs/2112.00739v1
• Date: Wed, 1 Dec 2021 14:28:15 GMT
• Title: Incomplete Multi-view Clustering via Cross-view Relation Transfer
• Authors: Yiming Wang, Dongxia Chang, Zhiqiang Fu, Yao Zhao
• Abstract summary: We propose a novel incomplete multi-view clustering framework, which incorporates cross-view relation transfer and multi-view fusion learning. Experiments conducted on several real datasets demonstrate the effectiveness of the proposed method.
• Score: 41.17336912278538
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete multi-view clustering, the view-missing problem increases the difficulty of learning common representations from different views. To address the challenge, we propose a novel incomplete multi-view clustering framework, which incorporates cross-view relation transfer and multi-view fusion learning. Specifically, based on the consistency existing in multi-view data, we devise a cross-view relation transfer-based completion module, which transfers known similar inter-instance relationships to the missing view and recovers the missing data via graph networks based on the transferred relationship graph. Then the view-specific encoders are designed to extract the recovered multi-view data, and an attention-based fusion layer is introduced to obtain the common representation. Moreover, to reduce the impact of the error caused by the inconsistency between views and obtain a better clustering structure, a joint clustering layer is introduced to optimize recovery and clustering simultaneously. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method.

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