AGPVC: Partially View-Aligned Clustering with Anchor Graph for Multi-View Data

Although the research of traditional multi-view clustering has made great progress, most methods still require ideally constructed datasets as input. However, in some real applications, the multi-view data may be partially aligned, leading to the problem of partially view-aligned clustering. Existing partially view-aligned clustering methods rely on numerous and expensive alignment information to obtain the promising results. Thus, we propose a novel method, termed partially view-aligned clustering with anchor graph (AGPVC) to tackle this problem, which can adapt to less alignment scenario favorably. Specifically, AGPVC employs anchors to reconstruct the inter-view alignment relationship, and further explores the within-view and cross-view consistency of correspondence to reduce the unreliable alignment in category-level. In this way, reliable alignment across views can be obtained. Different from the existing post-concatenation methods, the reconstructed correspondences are exploited to constrain the recovered data by decoders, thus AGPVC learns a fusion representation for each view, which can integrate the information of other views while satisfy the alignment relationship. Experimental results on several real-world multi-view datasets confirm its superiority compared to other state-of-the-art methods for partially view-aligned clustering.