49 citations · 169 across the 17 of their papers we have counts for
17 papers
A Prototype-Based Neural Network for Image Anomaly Detection and Localization
Chao Huang, Zhao Kang, Hong Wu
Image anomaly detection and localization perform not only image-level anomaly classification but also locate pixel-level anomaly regions. Recently, it has received much research at…
Eliminating Gradient Conflict in Reference-based Line-Art Colorization
Zekun Li, Zhengyang Geng, Zhao Kang +2
Reference-based line-art colorization is a challenging task in computer vision. The color, texture, and shading are rendered based on an abstract sketch, which heavily relies on th…
Log-based Sparse Nonnegative Matrix Factorization for Data Representation
Chong Peng, Yiqun Zhang, Yongyong Chen +3
Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF,…
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization
Chong Peng, Yang Liu, Yongyong Chen +5
In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximatio…
Self-supervised Consensus Representation Learning for Attributed Graph
Changshu Liu, Liangjian Wen, Zhao Kang +2
Attempting to fully exploit the rich information of topological structure and node features for attributed graph, we introduce self-supervised learning mechanism to graph represent…
Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view
Zhao Kang, Zhiping Lin, Xiaofeng Zhu +1
Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: encounter the expensive time overhead, fail in exp…
Kernel Two-Dimensional Ridge Regression for Subspace Clustering
Chong Peng, Qian Zhang, Zhao Kang +2
Subspace clustering methods have been widely studied recently. When the inputs are 2-dimensional (2D) data, existing subspace clustering methods usually convert them into vectors,…
Structured Graph Learning for Clustering and Semi-supervised Classification
Zhao Kang, Chong Peng, Qiang Cheng +4
Graphs have become increasingly popular in modeling structures and interactions in a wide variety of problems during the last decade. Graph-based clustering and semi-supervised cla…
Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering
Chong Peng, Zhilu Zhang, Zhao Kang +2
In this paper, we propose a new Semi-Nonnegative Matrix Factorization method for 2-dimensional (2D) data, named TS-NMF. It overcomes the drawback of existing methods that seriously…
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…
A Fast Factorization-based Approach to Robust PCA
Chong Peng, Zhao Kang, Qiang Chen
Robust principal component analysis (RPCA) has been widely used for recovering low-rank matrices in many data mining and machine learning problems. It separates a data matrix into…
Robust Subspace Clustering via Smoothed Rank Approximation
Zhao Kang, Chong Peng, Qiang Cheng
Matrix rank minimizing subject to affine constraints arises in many application areas, ranging from signal processing to machine learning. Nuclear norm is a convex relaxation for t…