Selective Contrastive Learning for Unpaired Multi-View Clustering.

In this article, we investigate a novel but insufficiently studied issue, unpaired multi-view clustering (UMC), where no paired observed samples exist in multi-view data, and the goal is to leverage the unpaired observed samples in all views for effective joint clustering. Existing methods in incomplete multi-view clustering usually utilize the sample pairing relationship between views to connect the views for joint clustering, but unfortunately, it is invalid for the UMC case. Therefore, we strive to mine a consistent cluster structure between views and propose an effective method, namely selective contrastive learning for UMC (scl-UMC), which needs to solve the following two challenging issues: 1) uncertain clustering structure under no supervision information and 2) uncertain pairing relationship between the clusters of views. Specifically, for the first one, we design an inner-view (IV) selective contrastive learning module to enhance the clustering structures and alleviate the uncertainty, which selects confident samples near the cluster centroids to perform contrastive learning in each view. For the second one, we design a cross-view (CV) selective contrastive learning module to first iteratively match the clusters between views and then tighten the matched clusters. Also, we utilize mutual information to further enhance the correlation of the matched clusters between views. Extensive experiments show the efficiency of our methods for UMC, compared with the state-of-the-art methods.

Iterative Multiview Subspace Learning for Unpaired Multiview Clustering.

In real applications, several unpredictable or uncertain factors could result in unpaired multiview data, i.e., the observed samples between views cannot be matched. Since joint clustering among views is more effective than individual clustering in each view, we investigate unpaired multiview clustering (UMC), which is a valuable but insufficiently studied problem. Due to lack of matched samples between views, we could fail to build the connection between views. Therefore, we aim to learn the latent subspace shared by views. However, existing multiview subspace learning methods usually rely on the matched samples between views. To address this issue, we propose an iterative multiview subspace learning strategy iterative unpaired multiview clustering (IUMC), aiming to learn a complete and consistent subspace representation among views for UMC. Moreover, based on IUMC, we design two effective UMC methods: 1) Iterative unpaired multiview clustering via covariance matrix alignment (IUMC-CA) that further aligns the covariance matrix of subspace representations and then performs clustering on the subspace and 2) iterative unpaired multiview clustering via one-stage clustering assignments (IUMC-CY) that performs one-stage multiview clustering (MVC) by replacing the subspace representations with clustering assignments. Extensive experiments show the excellent performance of our methods for UMC, compared with the state-of-the-art methods. Also, the clustering performance of observed samples in each view can be considerably improved by those observed samples from the other views. In addition, our methods have good applicability in incomplete MVC.