Error-Resilient incomplete multi-View clustering: Mitigating imputation-induced error accumulation

Abstract

Incomplete Multi-View Clustering (IMC) plays a pivotal role in integrating and analyzing multi-view data with missing information. Most of existing IMC methods improve clustering performance by inherently incorporating a data recovery step to derive a common representation or consensus graph. However, the imputation of missing data may introduce biased errors, which can accumulate and amplify during iterative optimization, ultimately distorting clustering results. To tackle this critical issue, we propose a novel unified optimization framework that jointly learns data completion and error removal in a mutually reinforcing manner. Specifically, our method introduces a dual-path architecture: one path reconstructs missing views via self-representation, while the other path explicitly models and eliminates biased errors. Crucially, these two components interact via an alternating minimization scheme, enabling them to mutually enhance each other. This synergy effectively reduces error accumulation, leading to a more accurate graph for clustering. Experiments on real-world datasets show that the proposed framework achieves state-of-the-art performance under extremely high missing rates (up to 90 %), significantly reducing error propagation while outperforming existing baselines.