Traditional subspace clustering methods usually replace the matrix rank function by the matrix kernel norm to recover the original low rank matrices. However, in the process of minimizing the matrix kernel norm, these algorithms pay too much attention to the calculation of the large singular values of the matrix, resulting in inaccurate estimation of the matrix rank. To this end, this paper analyzed the long tail distribution of matrix singular values and proposed a low rank subspace clustering model based on truncated Schatten-p norm. The proposed model fitted the long tail distribution of matrix singular va-lues well and toke full account of the contribution of small singular values to the process of low rank matrix recovery. The mo-del could make full use of small singular values to fit the long tail distribution of matrix singular values, ultimately achieved an accurate estimation of matrix rank function and improved the performance of subspace clustering. The experimental results show that, compared with the WNNM-LRR and BDR subspace clustering algorithms, the proposed method improves the clustering accuracy by 11% and 8% on Extended Yale B dataset, respectively. The proposed method can better fit the distribution of data singular values and construct the similarity matrices more accurately.

[2023-07-25] Accepted Paper /doc/2023.04.0219/v1

[2024-01-05] Printed Article /doc/2023.04.0219/v2