Deep multi-view clustering with diverse and discriminative feature learning

Abstract

Multi-view clustering (MVC) has gained significant attention in unsupervised learning. However, existing methods often face two key limitations: (1) many approaches rely on feature fusion from all views to identify cluster patterns, which inevitably reduces the distinctiveness of the learned representations; (2) existing methods primarily focus on uncovering common semantic features across different views while neglecting to promote the diversity of representations. As a result, they fail to fully leverage the complementary information across views, which potentially inhibits the effectiveness of representation learning. To address these challenges, we propose a novel diverse and discriminative feature learning framework for deep multi-view clustering (DDMVC) in a fusion-free manner. Specifically, we introduce a consistency constraint that performs preliminary alignment of low-level features to ensure consistent relationships between samples from different views. Following this, our model leverages contrastive learning to achieve consistency across multiple views and enhances the diversity of multi-view representations by ensuring the embedding vectors of samples within a batch to be distinct and by decorrelating the embedding dimensions (or variables). In this way, the proposed model can preserve the information content of each view at a certain level and reduce redundancy across multiple views, thereby facilitating the exploration of underlying complementarity among views. This approach successfully incorporates dimension independence in contrastive learning and can be easily integrated into other deep neural networks. Extensive evaluations on eight widely used benchmark datasets demonstrate that the proposed approach outperforms several state-of-the-art MVC methods. The code is available at https://github.com/xujunpeng832/DDMVC.