305 citations · 1k across the 30 of their papers we have counts for
Showing 2015Show all
3 papers · 1 filter
cs.CV2015★ 142 cited
Robust PCA via Nonconvex Rank Approximation
Zhao Kang, Chong Peng, Qiang Cheng
Numerous applications in data mining and machine learning require recovering a matrix of minimal rank. Robust principal component analysis (RPCA) is a general framework for handlin…
cs.CV2015★ 25 cited
Robust Subspace Clustering via Tighter Rank Approximation
Zhao Kang, Chong Peng, Qiang Cheng
Matrix rank minimization problem is in general NP-hard. The nuclear norm is used to substitute the rank function in many recent studies. Nevertheless, the nuclear norm approximatio…
cs.CV2015★ 41 cited
LogDet Rank Minimization with Application to Subspace Clustering
Zhao Kang, Chong Peng, Jie Cheng +1
Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. Howeve…