Commonness and Inconsistency Learning with Structure Constrained Adaptive Loss Minimization for Multi-View Clustering

Subspace clustering has emerged as a prominent research focus, demonstrating remarkable potential in handling multi-view data by effectively harnessing their diverse and information-rich features. In this study, we present a novel framework for multi-view subspace clustering that addresses several critical aspects of the problem. Our approach introduces three key innovations: First, we propose a dual-component representation model that simultaneously considers both consistent and inconsistent elements across different views. The consistent component is designed to capture shared structural patterns with robust commonality, while the inconsistent component effectively models view-specific variations through sparsity constraints across multiple modes. Second, we implement cross-mode sparsity constraints that enable the inconsistent component to efficiently extract high-order information from the data. This design not only enhances the representation capability of the inconsistent component but also facilitates the consistent component in revealing high-order structural relationships within the data. Third, we develop an adaptive loss function that offers greater flexibility in handling noise and outliers, thereby significantly improving the model’s robustness in real-world applications. Through extensive experimentation, we demonstrate that our proposed method consistently outperforms existing approaches, achieving superior clustering performance across various benchmark datasets. The experimental results comprehensively validate the effectiveness and advantages of our approach in terms of clustering accuracy, robustness, and computational efficiency.