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Chang-Dong Wang

Chang-Dong Wang

D-Index & Metrics

Computer Science

D-Index
44
Citations
7389
World Ranking
7619
National Ranking
1003

Overview

Chang-Dong Wang is affiliated with Sun Yat-sen University in China and has contributed significantly to the field of computer science, with a particular focus on artificial intelligence and related subfields.

The scientist's recent publications reflect a research emphasis on multi-view clustering and advanced machine learning techniques. Notable papers include:

  • Multi-View Clustering in Latent Embedding Space, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Fast Multi-View Clustering Via Ensembles: Towards Scalability, Superiority, and Simplicity, 2023, IEEE Transactions on Knowledge and Data Engineering
  • Efficient Multi-View Clustering via Unified and Discrete Bipartite Graph Learning, 2023, IEEE Transactions on Neural Networks and Learning Systems
  • Seeking commonness and inconsistencies: A jointly smoothed approach to multi-view subspace clustering, 2022, Information Fusion
  • Representation Learning in Multi-view Clustering: A Literature Review, 2022, Data Science and Engineering

Frequent co-authors of Chang-Dong Wang include:

  • Jianhuang Lai
  • Dong Huang
  • Man-Sheng Chen
  • Ling Huang
  • Philip S. Yu

The scientist regularly publishes in several venues, reflecting a focus on neural networks, knowledge engineering, and computational intelligence:

  • arXiv (Cornell University)
  • IEEE Transactions on Neural Networks and Learning Systems
  • Neural Networks
  • IEEE Transactions on Knowledge and Data Engineering
  • IEEE Transactions on Emerging Topics in Computational Intelligence

Chang-Dong Wang has published books with Springer Science+Business Media, including "Big Data" (2022) and "Advanced Data Mining and Applications" (2020).

The scientist's main field of study is computer science, with specialization in several subfields:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Statistical and Nonlinear Physics
  • Urban Studies

Research topics prominently covered in Chang-Dong Wang's work include:

  • Advanced Graph Neural Networks
  • Recommender Systems and Techniques
  • Face and Expression Recognition
  • Advanced Clustering Algorithms Research
  • Text and Document Classification Technologies
  • Topic Modeling
  • Complex Network Analysis Techniques

Best Publications

  • Ultra-Scalable Spectral Clustering and Ensemble Clustering

    Dong Huang;Chang-Dong Wang;Jian-Sheng Wu;Jian-Huang Lai

  • Locally Weighted Ensemble Clustering

    Dong Huang;Chang-Dong Wang;Jian-Huang Lai

  • Generative Dual Adversarial Network for Generalized Zero-Shot Learning

    He Huang;Changhu Wang;Philip S. Yu;Chang-Dong Wang

  • Multi-View Clustering in Latent Embedding Space

    Man-Sheng Chen;Ling Huang;Chang-Dong Wang;Dong Huang

  • Fast Multi-View Clustering Via Ensembles: Towards Scalability, Superiority, and Simplicity

    Unknown

  • Weighted Multi-view Clustering with Feature Selection

    Yu-Meng Xu;Chang-Dong Wang;Jian-Huang Lai

  • Robust Ensemble Clustering Using Probability Trajectories

    Dong Huang;Jian-Huang Lai;Chang-Dong Wang

  • Enhanced Ensemble Clustering via Fast Propagation of Cluster-Wise Similarities

    Dong Huang;Chang-Dong Wang;Hongxing Peng;Jianhuang Lai

  • DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System

    Zhi-Hong Deng;Ling Huang;Chang-Dong Wang;Jian-Huang Lai

  • Ensemble clustering using factor graph

    Dong Huang;Jianhuang Lai;Chang-Dong Wang

  • Multi-View Clustering Based on Belief Propagation

    Chang-Dong Wang;Jian-Huang Lai;Philip S. Yu

  • Representation Learning in Multi-view Clustering: A Literature Review

    Unknown

  • Multi-Exemplar Affinity Propagation

    Chang-Dong Wang;Jian-Huang Lai;Ching Y. Suen;Jun-Yong Zhu

  • Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering

    Chang-Dong Wang;Zhi-Hong Deng;Jian-Huang Lai;Philip S. Yu

  • Low-Rank Tensor Based Proximity Learning for Multi-View Clustering

    Unknown

  • Combining multiple clusterings via crowd agreement estimation and multi-granularity link analysis

    Dong Huang;Jian-Huang Lai;Chang-Dong Wang

  • Seeking commonness and inconsistencies: A jointly smoothed approach to multi-view subspace clustering

    Unknown

  • EdMot: An Edge Enhancement Approach for Motif-aware Community Detection

    Pei-Zhen Li;Ling Huang;Chang-Dong Wang;Jian-Huang Lai

  • Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency

    Youwei Liang;Dong Huang;Chang-Dong Wang;Philip S. Yu

  • SVStream: A Support Vector-Based Algorithm for Clustering Data Streams

    Chang-Dong Wang;Jian-Huang Lai;Dong Huang;Wei-Shi Zheng

  • One-step Kernel Multi-view Subspace Clustering

    Guang-Yu Zhang;Yu-Ren Zhou;Yu-Ren Zhou;Xiao-Yu He;Chang-Dong Wang

  • Multi-view intact space clustering

    Ling Huang;Hong-Yang Chao;Chang-Dong Wang

  • Efficient Orthogonal Multi-view Subspace Clustering

    Unknown

  • An ACO-based Scheduling Strategy on Load Balancing in Cloud Computing Environment

    Wei-Tao Wen;Chang-Dong Wang;De-Shen Wu;Ying-Yan Xie

  • Robust Ensemble Clustering Using Probability Trajectories

    Dong Huang;Jian-Huang Lai;Chang-Dong Wang

  • Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multi-view Clustering

    Youwei Liang;Dong Huang;Chang-Dong Wang

  • Multi-view Intact Space Clustering

    Ling Ling;Hong-Yang Chao;Chang-Dong Wang

Frequent Co-Authors

Jianhuang Lai
Jianhuang Lai Sun Yat-sen University
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Chee Keong Kwoh
Chee Keong Kwoh Nanyang Technological University
Wei-Shi Zheng
Wei-Shi Zheng Sun Yat-sen University
Changhu Wang
Changhu Wang ByteDance
Yuanqing Li
Yuanqing Li South China University of Technology
Jiliang Tang
Jiliang Tang Michigan State University
Pong C. Yuen
Pong C. Yuen Hong Kong Baptist University
Zhenan Sun
Zhenan Sun Chinese Academy of Sciences
Charu C. Aggarwal
Charu C. Aggarwal IBM (United States)

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