robust subspace clustering via half-quadratic minimization Half-quadratic mini-mization is provided as an efficient solution to the proposedrobust subspace clustering formulations Experimental re-sults on Hopkins 155 dataset and Extended Yale DatabaseB demonstrate that our method outperforms state-of-the-artsubspace clustering methods 1
Robust Subspace Clustering With Complex Noise - IEEE Xplore Half-quadratic minimization is provided as an efficient solution to the proposed robust subspace clustering formulations Experimental results on three commonly used data sets show that our method outperforms state-of-the-art subspace clustering methods
ROBUST SUBSPACE CLUSTERING - Project Euclid Here, the subspace clustering problem is formulated as a nonconvex optimization problem over the choice of bases for each subspace as well as a set of variables indicating the correct segmentation
[1301. 2603] Robust subspace clustering - arXiv. org This paper introduces an algorithm inspired by sparse subspace clustering (SSC) [In IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2009) 2790-2797] to cluster noisy data, and develops some novel theory demonstrating its correctness
Correntropy Induced L2 Graph for Robust Subspace Clustering In this paper, we study the robust subspace clustering problem, and present a general framework from the view-point of half-quadratic optimization to unify the L1 norm, Frobenius norm, L21 norm and nuclear norm based sub-space clustering methods