Multi-view Granular-ball Contrastive Clustering
This addresses limitations in multi-view clustering for data analysis, but it is incremental as it builds on existing contrastive learning methods.
The paper tackles the problem of false negatives and overlooked local structures in multi-view contrastive learning by proposing Multi-view Granular-ball Contrastive Clustering (MGBCC), which segments samples into granular balls and reinforces associations in a shared latent space, achieving improved performance as validated through extensive experiments.
Previous multi-view contrastive learning methods typically operate at two scales: instance-level and cluster-level. Instance-level approaches construct positive and negative pairs based on sample correspondences, aiming to bring positive pairs closer and push negative pairs further apart in the latent space. Cluster-level methods focus on calculating cluster assignments for samples under each view and maximize view consensus by reducing distribution discrepancies, e.g., minimizing KL divergence or maximizing mutual information. However, these two types of methods either introduce false negatives, leading to reduced model discriminability, or overlook local structures and cannot measure relationships between clusters across views explicitly. To this end, we propose a method named Multi-view Granular-ball Contrastive Clustering (MGBCC). MGBCC segments the sample set into coarse-grained granular balls, and establishes associations between intra-view and cross-view granular balls. These associations are reinforced in a shared latent space, thereby achieving multi-granularity contrastive learning. Granular balls lie between instances and clusters, naturally preserving the local topological structure of the sample set. We conduct extensive experiments to validate the effectiveness of the proposed method.