Centralized joint sparse representation for multi-view subspace clustering

摘要

Multi-view subspace clustering arises in many computer visional tasks such as object recognition and image segmentation. The basic idea is to measure the same instance with multiple views. In this paper, we proposed two centralized joint sparse representation models, namely, Centralized Global Joint Sparse Representation (CGJSR) and Centralized Local Joint Sparse Representation (CLJSR) for multi-view subspace clustering. CGJSR and CLJSR force the concatenated representation matrix of all views and the representation matrix of each view to be sparse respectively. Both CGJSR and CLJSR allow the sparse coefficient matrix to approach a unified latent structure with an acceptable error. Noises and outliers regularization terms are included in CGJSR and CLJSR to reduce the influence of noises and outliers. Related optimization problems are solved using the alternating direction method of multipliers. Compared with seven state-of-the-art multi-view clustering algorithms, our proposed algorithms can achieve better or comparable results on four real-world datasets.