p norm and Lq norm minimization" />
6 June 2017 Robust subspace clustering via joint weighted Schatten- p norm and Lq norm minimization
Author Affiliations +
Abstract
Low-rank representation (LRR) has been successfully applied to subspace clustering. However, the nuclear norm in the standard LRR is not optimal for approximating the rank function in many real-world applications. Meanwhile, the L 21 norm in LRR also fails to characterize various noises properly. To address the above issues, we propose an improved LRR method, which achieves low rank property via the new formulation with weighted Schatten- p norm and L q norm (WSPQ). Specifically, the nuclear norm is generalized to be the Schatten- p norm and different weights are assigned to the singular values, and thus it can approximate the rank function more accurately. In addition, L q norm is further incorporated into WSPQ to model different noises and improve the robustness. An efficient algorithm based on the inexact augmented Lagrange multiplier method is designed for the formulated problem. Extensive experiments on face clustering and motion segmentation clearly demonstrate the superiority of the proposed WSPQ over several state-of-the-art methods.
Tao Zhang, Zhenmin Tang, Qing Liu, "Robust subspace clustering via joint weighted Schatten- p norm and Lq norm minimization," Journal of Electronic Imaging 26(3), 033021 (6 June 2017). https://doi.org/10.1117/1.JEI.26.3.033021 . Submission: Received: 23 January 2017; Accepted: 15 May 2017
Received: 23 January 2017; Accepted: 15 May 2017; Published: 6 June 2017
JOURNAL ARTICLE
15 PAGES


SHARE
Back to Top