Incomplete Multi-view Clustering via Cross-view Relation Transfer

TL;DR

This paper introduces a novel framework for incomplete multi-view clustering that leverages cross-view relation transfer and fusion learning to effectively handle missing data and improve clustering accuracy.

Contribution

It proposes a new incomplete multi-view clustering method using cross-view relation transfer and joint optimization for recovery and clustering.

Findings

Effective in recovering missing view data.

Improves clustering performance on real datasets.

Outperforms existing methods in handling incomplete views.

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.