305 citations · 1k across the 30 of their papers we have counts for
8 papers · 1 filter
Smoothed Multi-View Subspace Clustering
Peng Chen, Liang Liu, Zhengrui Ma +1
In recent years, multi-view subspace clustering has achieved impressive performance due to the exploitation of complementary imformation across multiple views. However, multi-view…
Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering
Zhengrui Ma, Zhao Kang, Guangchun Luo +1
Finding a suitable data representation for a specific task has been shown to be crucial in many applications. The success of subspace clustering depends on the assumption that the…
Pseudo-supervised Deep Subspace Clustering
Juncheng Lv, Zhao Kang, Xiao Lu +1
Auto-Encoder (AE)-based deep subspace clustering (DSC) methods have achieved impressive performance due to the powerful representation extracted using deep neural networks while pr…
Robust Graph Learning from Noisy Data
Zhao Kang, Haiqi Pan, Steven C. H. Hoi +1
Learning graphs from data automatically has shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause t…
Two Birds with One Stone: Transforming and Generating Facial Images with Iterative GAN
Dan Ma, Bin Liu, Zhao Kang +3
Generating high fidelity identity-preserving faces with different facial attributes has a wide range of applications. Although a number of generative models have been developed to…
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…
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…
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…