Explicit View-labels Matter: A Multifacet Complementarity Study of Multi-view Clustering

TL;DR

This paper introduces MCMVC, a multi-view clustering framework that explicitly incorporates view-label information across multiple facets, significantly improving clustering performance over state-of-the-art methods.

Contribution

It is the first to explicitly embed view-labels into multi-view clustering to enhance complementarity learning across multiple facets, including feature, view-label, and contrast.

Findings

MCMVC outperforms SOTA methods by over 5-7% on Caltech101-20.

Explicit view-label guidance improves multi-view clustering effectiveness.

All facets, especially view-label, contribute significantly to clustering performance.

Abstract

Consistency and complementarity are two key ingredients for boosting multi-view clustering (MVC). Recently with the introduction of popular contrastive learning, the consistency learning of views has been further enhanced in MVC, leading to promising performance. However, by contrast, the complementarity has not received sufficient attention except just in the feature facet, where the Hilbert Schmidt Independence Criterion term or the independent encoder-decoder network is usually adopted to capture view-specific information. This motivates us to reconsider the complementarity learning of views comprehensively from multiple facets including the feature-, view-label- and contrast- facets, while maintaining the view consistency. We empirically find that all the facets contribute to the complementarity learning, especially the view-label facet, which is usually neglected by existing methods. Based on this, a simple yet effective \underline{M}ultifacet \underline{C}omplementarity learning framework for \underline{M}ulti-\underline{V}iew \underline{C}lustering (MCMVC) is naturally developed, which fuses multifacet complementarity information, especially explicitly embedding the view-label information. To our best knowledge, it is the first time to use view-labels explicitly to guide the complementarity learning of views. Compared with the SOTA baselines, MCMVC achieves remarkable improvements, e.g., by average margins over $5.00\%$ and $7.00\%$ respectively in complete and incomplete MVC settings on Caltech101-20 in terms of three evaluation metrics.