
Selfsupervised Consensus Representation Learning for Attributed Graph
Attempting to fully exploit the rich information of topological structur...
read it

Selfpaced Principal Component Analysis
Principal Component Analysis (PCA) has been widely used for dimensionali...
read it

Towards Clusteringfriendly Representations: Subspace Clustering via Graph Filtering
Finding a suitable data representation for a specific task has been show...
read it

Pseudosupervised Deep Subspace Clustering
AutoEncoder (AE)based deep subspace clustering (DSC) methods have achi...
read it

Structured Graph Learning for Scalable Subspace Clustering: From Singleview to Multiview
Graphbased subspace clustering methods have exhibited promising perform...
read it

Kernel TwoDimensional Ridge Regression for Subspace Clustering
Subspace clustering methods have been widely studied recently. When the ...
read it

Structured Graph Learning for Clustering and Semisupervised Classification
Graphs have become increasingly popular in modeling structures and inter...
read it

RelationGuided Representation Learning
Deep autoencoders (DAEs) have achieved great success in learning data r...
read it

TwoDimensional SemiNonnegative Matrix Factorization for Clustering
In this paper, we propose a new SemiNonnegative Matrix Factorization me...
read it

Multiview Subspace Clustering via Partition Fusion
Multiview clustering is an important approach to analyze multiview dat...
read it

Structure Learning with Similarity Preserving
Leveraging on the underlying lowdimensional structure of data, lowrank...
read it

Largescale Multiview Subspace Clustering in Linear Time
A plethora of multiview subspace clustering (MVSC) methods have been pr...
read it

Multigraph Fusion for Multiview Spectral Clustering
A panoply of multiview clustering algorithms has been developed to deal...
read it

Multiple Partitions Aligned Clustering
Multiview clustering is an important yet challenging task due to the di...
read it

Latent Multiview SemiSupervised Classification
To explore underlying complementary information from multiple views, in ...
read it

Nonnegative Matrix Factorization with Local Similarity Learning
Existing nonnegative matrix factorization methods focus on learning glob...
read it

Clustering with Similarity Preserving
Graphbased clustering has shown promising performance in many tasks. A ...
read it

RESPCA: A Scalable Approach to Recovering Lowrank Matrices
Robust principal component analysis (RPCA) has drawn significant attenti...
read it

Lowrank Kernel Learning for Graphbased Clustering
Constructing the adjacency graph is fundamental to graphbased clusterin...
read it

Similarity Learning via Kernel Preserving Embedding
Data similarity is a key concept in many datadriven applications. Many ...
read it

Robust Graph Learning from Noisy Data
Learning graphs from data automatically has shown encouraging performanc...
read it

Selfweighted Multiple Kernel Learning for Graphbased Clustering and Semisupervised Classification
Multiple kernel learning (MKL) method is generally believed to perform b...
read it

Two Birds with One Stone: Iteratively Learn Facial Attributes with GANs
Generating high fidelity identitypreserving faces has a wide range of a...
read it

Unified Spectral Clustering with Optimal Graph
Spectral clustering has found extensive use in many areas. Most traditio...
read it

Twin Learning for Similarity and Clustering: A Unified Kernel Approach
Many similaritybased clustering methods work in two separate steps incl...
read it

A Fast Factorizationbased Approach to Robust PCA
Robust principal component analysis (RPCA) has been widely used for reco...
read it

TopN Recommendation on Graphs
Recommender systems play an increasingly important role in online applic...
read it

TopN Recommendation with Novel Rank Approximation
The importance of accurate recommender systems has been widely recognize...
read it

TopN Recommender System via Matrix Completion
TopN recommender systems have been investigated widely both in industry...
read it

Robust PCA via Nonconvex Rank Approximation
Numerous applications in data mining and machine learning require recove...
read it

Robust Subspace Clustering via Tighter Rank Approximation
Matrix rank minimization problem is in general NPhard. The nuclear norm...
read it

Robust Subspace Clustering via Smoothed Rank Approximation
Matrix rank minimizing subject to affine constraints arises in many appl...
read it

LogDet Rank Minimization with Application to Subspace Clustering
Lowrank matrix is desired in many machine learning and computer vision ...
read it
Zhao Kang
is this you? claim profile
Assistant Professor at University of Electronic Science and Technology since 2017, DRA Fellow at Southern Illinois University Carbondale 20162017, PHD Student, Computer Science at Southern Illinois University Carbondale from 20132017, Research Assistant at Physics, NIST, University of Colorado at Boulder 2012, Research Assistant at Sichuan University from 20082011.