Sample-Dependent Subspace Clustering with Elastic Structure Consistency Constraints

Subspace clustering (SC) approximates high-dimensional data as a combination of low-dimensional subspaces, which is suitable for high-dimensional data analysis across various domains including image segmentation and face recognition. Existing SC methods typically obtain the global structure representation solely through the self-representation of the samples, thereby neglecting the intrinsic local connections among the samples. Moreover, due to their inherent framework design, obtaining additional a priori information in unsupervised scenarios presents a significant challenge. To address these limitations, this paper proposes a new method, named Sample-Dependent Subspace Clustering with Elastic Structure Consistency Constraints (SDSC). Firstly, we introduce a new Elastic Structure Consistency Constraints (ESCC) strategy to measure global and local structures elastically. Benefiting from this strategy, SDSC can flexibly explore the structural information within the samples to obtain a comprehensive data representation. By employing the joint regularization term, SDSC can learn effective cluster assignment information directly from the constrained structured data representation, and the cluster assignment information and representation coefficient matrix are smoothly integrated into a unified framework and learn in a mutually reinforcing manner. This learning approach contributes to comprehensive and high-quality clustering results, enhancing the robustness and utility of SDSC. Extensive experiments on several real-world benchmarks and synthetic datasets demonstrate the feasibility and effectiveness of SDSC.