Efficient subspace clustering based on self-representation and grouping effect

Shichao Zhang, Yonggang Li, Debo Cheng, Zhenyun Deng, Lifeng Yang

Abstract: Traditional subspace clustering methods [such as sparse subspace clustering (SSC), least squares representation (LSR) and smooth representation clustering] either considered the grouping effect or the sparsity to group original data into clusters. This paper demonstrates the necessary of both the grouping effect and the sparsity for conducting subspace clustering, by proposing a new Self-Representation and Subspace Clustering based on Grouping Effect (SRGE) method. Specifically, first of all, a row sparse $$\ell_{2,1}$$ ℓ 2 , 1 -norm regularizer is utilized to represent each sample by other samples. Then, the grouping effect of the data is designed to ensure that the coefficient of close samples is similar, aiming at generating a diagonal block self-representation coefficient matrix. Finally, an affinity matrix is obtained for conducting spectral clustering. The proposed method can be regarded as a trade-off between SSC and LSR. The experimental results of segmentation on real datasets showed that the proposed method significantly outperformed the state-of-the-art methods in terms of all kinds of evaluation metrics.