Subspace Clustering by Integrating Sparseness and Spatial-Closeness Priors
Zhe Li, Haodong Pei, Liang Ju He, Jiaming Liu, Jiaxin Hu, Dongji Wang · Journal of Physics Conference Series · 2020
Abstract How to construct an effective sample affinity matrix is an important problem for subspace clustering, and most existing subspace clustering algorithms pursue the affinity matrix in a single space. In this paper, we propose a novel computational framework for subspace clustering, called Complementary Subspace Clustering (CSC) at first, where the affinity matrix is constructed in a pair of complementary spaces which provide different and complementary constraints on the affinity matrix. Many existing structural priors on self representation and dimensionality reduction can be seamlessly integrated into the CSC framework. Then under this framework, we explore a simple and effective subspace clustering algorithm by respectively introducing two basic priors - sparse representation and spatial closeness - into the referred pair of spaces. Moreover, a kernel variant of the proposed clustering algorithm is present. Extensive experimental results demonstrate that although only basic priors are involved, the explored algorithms from the CSC framework can improve the clustering performances significantly when the number of the sample classes is relatively big.