49 citations · 169 across the 17 of their papers we have counts for
6 papers · 1 filter
Multi-view Subspace Clustering via Partition Fusion
Juncheng Lv, Zhao Kang, Boyu Wang +2
Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way. Among various methods, the multi-view subspace clustering approach has gained incr…
Structure Learning with Similarity Preserving
Zhao Kang, Xiao Lu, Yiwei Lu +2
Leveraging on the underlying low-dimensional structure of data, low-rank and sparse modeling approaches have achieved great success in a wide range of applications. However, in man…
Multi-graph Fusion for Multi-view Spectral Clustering
Zhao Kang, Guoxin Shi, Shudong Huang +4
A panoply of multi-view clustering algorithms has been developed to deal with prevalent multi-view data. Among them, spectral clustering-based methods have drawn much attention and…
Latent Multi-view Semi-Supervised Classification
Xiaofan Bo, Zhao Kang, Zhitong Zhao +2
To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most…
Nonnegative Matrix Factorization with Local Similarity Learning
Chong Peng, Zhao Kang, Chenglizhao Chen +1
Existing nonnegative matrix factorization methods focus on learning global structure of the data to construct basis and coefficient matrices, which ignores the local structure that…
RES-PCA: A Scalable Approach to Recovering Low-rank Matrices
Chong Peng, Chenglizhao Chen, Zhao Kang +2
Robust principal component analysis (RPCA) has drawn significant attentions due to its powerful capability in recovering low-rank matrices as well as successful appplications in va…