9 citations · 18 across the 4 of their papers we have counts for
6 papers
Improved Dual Correlation Reduction Network
Yue Liu, Sihang Zhou, Xinwang Liu +2
Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging…
Multi-view Clustering via Deep Matrix Factorization and Partition Alignment
Chen Zhang, Siwei Wang, Jiyuan Liu +5
Multi-view clustering (MVC) has been extensively studied to collect multiple source information in recent years. One typical type of MVC methods is based on matrix factorization to…
Multi-view Deep One-class Classification: A Systematic Exploration
Siqi Wang, Jiyuan Liu, Guang Yu +5
One-class classification (OCC), which models one single positive class and distinguishes it from the negative class, has been a long-standing topic with pivotal application to real…
Deep Distribution-preserving Incomplete Clustering with Optimal Transport
Mingjie Luo, Siwei Wang, Xinwang Liu +5
Clustering is a fundamental task in the computer vision and machine learning community. Although various methods have been proposed, the performance of existing approaches drops dr…
Deep Fusion Clustering Network
Wenxuan Tu, Sihang Zhou, Xinwang Liu +4
Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structu…
Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix
Weixuan Liang, Sihang Zhou, Jian Xiong +5
Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data by performing clustering on the learned optimal embedding across views. Though demo…