455 citations · 2.1k across the 21 of their papers we have counts for
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Deep Temporal Contrastive Clustering
Ying Zhong, Dong Huang, Chang-Dong Wang
Recently the deep learning has shown its advantage in representation learning and clustering for time series data. Despite the considerable progress, the existing deep time series…
Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph Learning
Si-Guo Fang, Dong Huang, Xiao-Sha Cai +3
Although previous graph-based multi-view clustering algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from…
Adaptively-weighted Integral Space for Fast Multiview Clustering
Man-Sheng Chen, Tuo Liu, Chang-Dong Wang +2
Multiview clustering has been extensively studied to take advantage of multi-source information to improve the clustering performance. In general, most of the existing works typica…
Strongly Augmented Contrastive Clustering
Xiaozhi Deng, Dong Huang, Ding-Hua Chen +2
Deep clustering has attracted increasing attention in recent years due to its capability of joint representation learning and clustering via deep neural networks. In its latest dev…
Vision Transformer for Contrastive Clustering
Hua-Bao Ling, Bowen Zhu, Dong Huang +3
Vision Transformer (ViT) has shown its advantages over the convolutional neural network (CNN) with its ability to capture global long-range dependencies for visual representation l…
Deep Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks
Yuankun Xu, Dong Huang, Chang-Dong Wang +1
Deep clustering has shown its promising capability in joint representation learning and clustering via deep neural networks. Despite the significant progress, the existing deep clu…
DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks
Dong Huang, Ding-Hua Chen, Xiangji Chen +2
Deep clustering has recently emerged as a promising technique for complex data clustering. Despite the considerable progress, previous deep clustering works mostly build or learn t…
Joint Multi-view Unsupervised Feature Selection and Graph Learning
Si-Guo Fang, Dong Huang, Chang-Dong Wang +1
Despite significant progress, previous multi-view unsupervised feature selection methods mostly suffer from two limitations. First, they generally utilize either cluster structure…
Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity
Dong Huang, Chang-Dong Wang, Jian-Huang Lai
Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restrict…
Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering
Xiaosha Cai, Dong Huang, Guang-Yu Zhang +1
Multi-view subspace clustering aims to discover the hidden subspace structures from multiple views for robust clustering, and has been attracting considerable attention in recent y…