Unpaired Multi-view Clustering via Reliable View Guidance

TL;DR

This paper introduces a novel unpaired multi-view clustering method that leverages reliable views to guide clustering across multiple views without requiring paired samples, significantly improving clustering accuracy.

Contribution

The paper proposes RG-UMC and RGs-UMC, innovative methods that utilize reliable views to enhance clustering in unpaired multi-view data, addressing the lack of pairing and label uncertainty.

Findings

Outperforms state-of-the-art by 24-29% in NMI

Effectively utilizes reliable views for guidance

Improves cluster structure certainty

Abstract

This paper focuses on unpaired multi-view 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 multi-view clustering, existing methods typically rely on sample pairing between views to capture their complementary. However, that 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: uncertain cluster structure due to lack of label and uncertain pairing relationship due to 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 reliable views and other views. Then we propose Reliable view Guidance with one reliable view (RG-UMC) and multiple reliable views (RGs-UMC) for 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 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 NMI, respectively.