Contrastive Continual Multi-view Clustering with Filtered Structural Fusion

TL;DR

This paper introduces CCMVC-FSF, a novel continual multi-view clustering method that uses filtered structural information and contrastive learning to address the stability-plasticity dilemma in sequential data collection scenarios.

Contribution

It proposes a new approach combining a data buffer with contrastive learning and filtered structural fusion to prevent catastrophic forgetting in continual multi-view clustering.

Findings

Outperforms existing methods in clustering accuracy.

Effectively mitigates catastrophic forgetting.

Theoretically linked to semi-supervised learning and knowledge distillation.

Abstract

Multi-view clustering thrives in applications where views are collected in advance by extracting consistent and complementary information among views. However, it overlooks scenarios where data views are collected sequentially, i.e., real-time data. Due to privacy issues or memory burden, previous views are not available with time in these situations. Some methods are proposed to handle it but are trapped in a stability-plasticity dilemma. In specific, these methods undergo a catastrophic forgetting of prior knowledge when a new view is attained. Such a catastrophic forgetting problem (CFP) would cause the consistent and complementary information hard to get and affect the clustering performance. To tackle this, we propose a novel method termed Contrastive Continual Multi-view Clustering with Filtered Structural Fusion (CCMVC-FSF). Precisely, considering that data correlations play a vital role in clustering and prior knowledge ought to guide the clustering process of a new view, we develop a data buffer with fixed size to store filtered structural information and utilize it to guide the generation of a robust partition matrix via contrastive learning. Furthermore, we theoretically connect CCMVC-FSF with semi-supervised learning and knowledge distillation. Extensive experiments exhibit the excellence of the proposed method.