Debiased and Denoised Projection Learning for Incomplete Multi-view Clustering

Multi-view clustering achieves outstanding performance but relies on the assumption of complete multi-view samples. However, certain views may be partially unavailable due to failures during acquisition or storage, resulting in distribution shifts across views. Although some incomplete multi-view clustering (IMVC) methods have been proposed, they still confront the following limitations: 1) Missing-view data imputation methods increase the unnecessary computational complexity; 2) Consensus representation imputation methods always ignore the inter-view distribution bias due to missing views. To tackle these issues, we propose a novel IMVC based on projection debiasing and denoising (PDD). Specifically, it utilizes the unbiased projection learned from complete views to refine the biased projection learned from data with missing views. Additionally, we introduce a robust contrastive learning for consensus projection to mitigate cluster collapse risk induced by misalignment noise. Comprehensive experiments demonstrate that PDD achieves superior performance compared with state-of-the-art methods.