Adaptive Graph Completion Based Incomplete Multi-View Clustering

doi:10.1109/TMM.2020.3013408

Adaptive Graph Completion Based Incomplete Multi-View Clustering

2021-01-01

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DOI：10.1109/TMM.2020.3013408

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In real-world applications, it is often that the collected multi-view data are incomplete, i.e., some views of samples are absent. Existing clustering methods for incomplete multi-view data all focus on obtaining a common representation or graph from the available views but neglect the hidden information of missing views and information imbalance of different views. To solve these problems, a novel method, called adaptive graph completion based incomplete multi-view clustering (AGC_IMC), is proposed in this paper. Specifically, AGC_IMC develops a joint framework for graph completion and consensus representation learning, which mainly contains three components, i.e., within-view preservation, between-view inferring, and consensus representation learning. To reduce the negative influence of information imbalance, AGC_IMC introduces some adaptive weights to balance the importance of different views during the consensus representation learning. Importantly, AGC_IMC has the potential to recover the similarity graphs of all views with the optimal cluster structure, which encourages it to obtain a more discriminative consensus representation. Experimental results on five well-known datasets show that AGC_IMC significantly outperforms the state-of-the-art methods.

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Clustering methods

Machine learning

Visualization

Task analysis

Optimization

Incomplete multi-view clustering

common representation

graph completion

similarity graph

Adaptive Graph Completion Based Incomplete Multi-View Clustering

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