Title: Debiased and Denoised Projection Learning for Incomplete Multi-view Clustering
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Incomplete multi-view clustering, projection debiasing and denoising, robust contrastive learning.

Abstract: 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 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.

TL;DR: We propose a consensus projection refinement strategy for deep incomplete multi-view clustering, aiming to mitigate cluster collapse risk induced by misalignment noise.