Quality-Aware Robust Multi-View Clustering for Heterogeneous Observation Noise

TL;DR

This paper introduces QARMVC, a novel multi-view clustering framework that effectively handles heterogeneous observation noise by leveraging reconstruction discrepancy to assess data quality and improve clustering robustness.

Contribution

The paper proposes a new quality-aware framework that quantifies and mitigates heterogeneous noise in multi-view clustering using an information bottleneck and hierarchical learning strategies.

Findings

QARMVC outperforms existing methods on five benchmark datasets.

It effectively handles varying levels of observation noise.

The approach improves clustering accuracy in noisy environments.

Abstract

Deep multi-view clustering has achieved remarkable progress but remains vulnerable to complex noise in real-world applications. Existing noisy robust methods predominantly rely on a simplified binary assumption, treating data as either perfectly clean or completely corrupted. This overlooks the prevalent existence of heterogeneous observation noise, where contamination intensity varies continuously across data. To bridge this gap, we propose a novel framework termed Quality-Aware Robust Multi-View Clustering (QARMVC). Specifically, QARMVC employs an information bottleneck mechanism to extract intrinsic semantics for view reconstruction. Leveraging the insight that noise disrupts semantic integrity and impedes reconstruction, we utilize the resulting reconstruction discrepancy to precisely quantify fine-grained contamination intensity and derive instance-level quality scores. These scores are integrated into a hierarchical learning strategy: at the feature level, a quality-weighted contrastive objective is designed to adaptively suppress the propagation of noise; at the fusion level, a high-quality global consensus is constructed via quality-weighted aggregation, which is subsequently utilized to align and rectify local views via mutual information maximization. Extensive experiments on five benchmark datasets demonstrate that QARMVC consistently outperforms state-of-the-art baselines, particularly in scenarios with heterogeneous noise intensities.