455 citations · 2.1k across the 21 of their papers we have counts for
21 papers
Learning Representations for Clustering via Partial Information Discrimination and Cross-Level Interaction
Hai-Xin Zhang, Dong Huang, Hua-Bao Ling +3
In this paper, we present a novel deep image clustering approach termed PICI, which enforces the partial information discrimination and the cross-level interaction in a joint learn…
One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering
Si-Guo Fang, Dong Huang, Chang-Dong Wang +1
The bipartite graph structure has shown its promising ability in facilitating the subspace clustering and spectral clustering algorithms for large-scale datasets. To avoid the post…
Heterogeneous Tri-stream Clustering Network
Xiaozhi Deng, Dong Huang, Chang-Dong Wang
Contrastive deep clustering has recently gained significant attention with its ability of joint contrastive learning and clustering via deep neural networks. Despite the rapid prog…
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…
Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency
Youwei Liang, Dong Huang, Chang-Dong Wang +1
Graph learning has emerged as a promising technique for multi-view clustering with its ability to learn a unified and robust graph from multiple views. However, existing graph lear…
Ultra-Scalable Spectral Clustering and Ensemble Clustering
Dong Huang, Chang-Dong Wang, Jian-Sheng Wu +2
This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-…
Enhanced Ensemble Clustering via Fast Propagation of Cluster-wise Similarities
Dong Huang, Chang-Dong Wang, Hongxing Peng +2
Ensemble clustering has been a popular research topic in data mining and machine learning. Despite its significant progress in recent years, there are still two challenging issues…
Toward Multidiversified Ensemble Clustering of High-Dimensional Data: From Subspaces to Metrics and Beyond
Dong Huang, Chang-Dong Wang, Jian-Huang Lai +1
The rapid emergence of high-dimensional data in various areas has brought new challenges to current ensemble clustering research. To deal with the curse of dimensionality, recently…
Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation
Dong Huang, Chang-Dong Wang, Jian-Huang Lai +3
Support vector clustering (SVC) is a versatile clustering technique that is able to identify clusters of arbitrary shapes by exploiting the kernel trick. However, one hurdle that r…
Robust Ensemble Clustering Using Probability Trajectories
Dong Huang, Jian-Huang Lai, Chang-Dong Wang
Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly…
Locally Weighted Ensemble Clustering
Dong Huang, Chang-Dong Wang, Jian-Huang Lai
Due to its ability to combine multiple base clusterings into a probably better and more robust clustering, the ensemble clustering technique has been attracting increasing attentio…
Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis
Dong Huang, Jian-Huang Lai, Chang-Dong Wang
The clustering ensemble technique aims to combine multiple clusterings into a probably better and more robust clustering and has been receiving an increasing attention in recent ye…