PROTOCOL: Partial Optimal Transport-enhanced Contrastive Learning for Imbalanced Multi-view Clustering

TL;DR

This paper introduces PROTOCOL, a novel contrastive learning framework that addresses class imbalance in multi-view clustering by leveraging partial optimal transport and class-rebalanced contrastive techniques, significantly improving performance.

Contribution

It pioneers the systematic study of imbalanced multi-view clustering and proposes a new framework combining partial optimal transport with contrastive learning to handle class imbalance.

Findings

Significantly improves clustering on imbalanced multi-view datasets.

Effectively perceives class imbalance via partial optimal transport.

Enhances minority sample representation through class-rebalanced contrastive learning.

Abstract

While contrastive multi-view clustering has achieved remarkable success, it implicitly assumes balanced class distribution. However, real-world multi-view data primarily exhibits class imbalance distribution. Consequently, existing methods suffer performance degradation due to their inability to perceive and model such imbalance. To address this challenge, we present the first systematic study of imbalanced multi-view clustering, focusing on two fundamental problems: i. perceiving class imbalance distribution, and ii. mitigating representation degradation of minority samples. We propose PROTOCOL, a novel PaRtial Optimal TranspOrt-enhanced COntrastive Learning framework for imbalanced multi-view clustering. First, for class imbalance perception, we map multi-view features into a consensus space and reformulate the imbalanced clustering as a partial optimal transport (POT) problem, augmented with progressive mass constraints and weighted KL divergence for class distributions. Second, we develop a POT-enhanced class-rebalanced contrastive learning at both feature and class levels, incorporating logit adjustment and class-sensitive learning to enhance minority sample representations. Extensive experiments demonstrate that PROTOCOL significantly improves clustering performance on imbalanced multi-view data, filling a critical research gap in this field.