Clustering noisy, high-dimensional, and structurally complex data has always been a challenging task. As most existing clustering methods are not able to deal with both the adverse impact of noisy samples and the complex structures of data, in this paper, we propose a novel Robust and Sparse Possibilistic K-Subspace Clustering algorithm (RSPKS) to integrate subspace recovery and possibilistic clustering algorithms under a unified sparse framework. First, the proposed method sparsifies the membership matrix and the subspace projection vector under a dual-sparse framework to handle high-dimensio...
