Unpaired Multiview Clustering via Reliable View Guidance.

Abstract

This article focuses on unpaired multiview clustering (UMC), a challenging problem, where paired observed samples are unavailable across multiple views. The goal is to perform effective joint clustering using the unpaired observed samples in all views. In incomplete multiview clustering (IMC), existing methods typically rely on sample pairing between views to capture their complementary. However, this is not applicable in the case of UMC. Hence, we aim to extract the consistent cluster structure across views. In UMC, two challenging issues arise: the uncertain cluster structure due to the lack of labels and the uncertain pairing relationship due to the absence of paired samples. We assume that the view with a good cluster structure is the reliable view, which acts as a supervisor to guide the clustering of the other views. With the guidance of reliable views, a more certain cluster structure of these views is obtained while achieving alignment between the reliable views and the other views. Then, we propose reliable view guided UMC with one reliable view (RG-UMC) and reliable view guided UMC with multiple reliable views (RGs-UMC). Specifically, we design alignment modules with one reliable view and multiple reliable views, respectively, to adaptively guide the optimization process. Also, we utilize the compactness module to enhance the relationship of samples within the same cluster. Meanwhile, an orthogonal constraint is applied to the latent representation to obtain discriminate features. Extensive experiments show that both RG-UMC and RGs-UMC outperform the best state-of-the-art method by an average of 24.14% and 29.42% in normalized mutual information (NMI), respectively.