Sparse sample self-representation for subspace clustering

This paper proposes a new subspace clustering method based on sparse sample self-representation (SSR). The proposed method considers SSR to solve the problem that affinity matrix does not strictly follow the structure of subspace, and also utilizes sparse constraint to ensure the robustness to noise and outliers in subspace clustering. Specifically, we propose to first construct a self-representation matrix for all samples and combine an l1-norm regularizer with an l2,1-norm regularizer to guarantee that each sample can be represented as a sparse linear combination of its related samples. Then, we conduct the resulting matrix to build an affinity matrix. Finally, we apply spectral clustering on the affinity matrix to conduct clustering. In order to validate the effectiveness of the proposed method, we conducted experiments on UCI datasets, and the experimental results showed that our proposed method reduced the minimal clustering error, outperforming the state-of-the-art methods.