Multiview Feature Decoupling for Deep Subspace Clustering

Abstract

Deep multi-view subspace clustering aims to reveal a common subspace structure by exploiting rich multi-view information. Despite promising progress, current methods focus only on multi-view consistency and complementarity, often overlooking the adverse influence of entangled superfluous information in features. Moreover, most existing works lack scalability and are inefficient for large-scale scenarios. To this end, we innovatively propose a deep subspace clustering method via Multi-view Feature Decoupling (MvFD). First, MvFD incorporates well-designed multi-type auto-encoders with self-supervised learning, explicitly decoupling consistent, complementary, and superfluous features for every view. The disentangled and interpretable feature space can then better serve unified representation learning. By integrating these three types of information within a unified framework, we employ information theory to obtain a minimal and sufficient representation with high discriminability. Besides, we introduce a deep metric network to model self-expression correlation more efficiently, where network parameters remain unaffected by changes in sample numbers. Extensive experiments show that MvFD yields State-of-the-Art performance in various types of multi-view datasets.