IBEC-MVC:INFORMATION BOTTLENECK EHANCED CONTRASTIVE MULTI-VIEW CLUSTERING VIA PROGRESSIVE MATRIX FACTORIZATION

Multi-view clustering has emerged as a fundamental problem in machine learning, aiming to discover hidden patterns by leveraging complementary information across multiple data representations. However, existing methods suffer from information loss during dimensionality reduction and fail to effectively balance view-specific characteristics with cross-view consistency. In this paper, we propose IBEC-MVC (Information Bottleneck Enhanced Contrastive Multi-View Clustering), a novel framework that integrates three key innovations: (1) Information Bottleneck-guided Progressive Matrix Factorization (IB-PMF) that systematically compresses representations while preserving clustering-relevant information through theoretically grounded constraints, (2) Entropy-Regularized Self-Supervised Contrastive Learning (ER-SCL) that enhances discriminative structure in latent representations while preventing cluster collapse, and (3) Mutual Information Maximization for View Consistency (MIM-VC) that optimally fuses multi-view information through principled information-theoretic objectives. Our theoretical analysis provides information preservation guarantees for the progressive factorization process, while extensive experiments on benchmark datasets demonstrate significant improvements over state-of-the-art methods.