activity
20192022
most citedSelf-supervised Consensus Representation Learning for Attributed Graph

49 citations · 73 across the 10 of their papers we have counts for

collaborators

12 papers

cs.LG2022

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,…

cs.SI202149 cited

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…

cs.LG202110 cited

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…

cs.CV2020

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,…

cs.LG2020

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…

cs.LG20203 cited

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…