World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
5834
World Ranking
9391
National Ranking
1189

Overview

Zhao Kang is affiliated with the University of Electronic Science and Technology of China in China. Their primary field of study is Computer Science, with a focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Computational Mechanics, and Media Technology.

Their research encompasses several key topics, including:

  • Face and Expression Recognition
  • Advanced Graph Neural Networks
  • Complex Network Analysis Techniques
  • Advanced Clustering Algorithms Research
  • Sparse and Compressive Sensing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Remote-Sensing Image Classification

Zhao Kang has contributed extensively to scholarly publications, with notable recent papers such as:

  • Large-Scale Multi-View Subspace Clustering in Linear Time, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview, 2021, IEEE Transactions on Cybernetics
  • Structured graph learning for clustering and semi-supervised classification, 2020, Pattern Recognition

Their work has appeared frequently in venues including:

  • arXiv (Cornell University)
  • Knowledge-Based Systems
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Information Sciences
  • Pattern Recognition

Zhao Kang collaborates often with several coauthors, with whom multiple joint publications have been produced. Frequent collaborators include:

  • Chong Peng
  • Ling Tian
  • Zenglin Xu
  • Qiang Cheng
  • Chenglizhao Chen

The scope of Zhao Kang's research integrates advanced methodologies in clustering algorithms and graph learning, reflected in their published studies on subspace clustering and graph-based machine learning approaches. Publications in leading journals and extensive contributions to arXiv highlight an ongoing engagement with the evolution of artificial intelligence and data-driven techniques.

Best Publications

  • Large-Scale Multi-View Subspace Clustering in Linear Time

    Zhao Kang;Wangtao Zhou;Zhitong Zhao;Junming Shao

  • Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview.

    Zhao Kang;Zhiping Lin;Xiaofeng Zhu;Wenbo Xu

  • Robust Graph Learning From Noisy Data

    Zhao Kang;Haiqi Pan;Steven C. H. Hoi;Zenglin Xu

  • Multi-graph fusion for multi-view spectral clustering

    Zhao Kang;Guoxin Shi;Shudong Huang;Wenyu Chen

  • Partition level multiview subspace clustering.

    Zhao Kang;Xinjia Zhao;Chong Peng;Hongyuan Zhu

  • Pseudo-Supervised Deep Subspace Clustering

    Juncheng Lv;Zhao Kang;Xiao Lu;Zenglin Xu

  • Auto-weighted multi-view clustering via kernelized graph learning

    Shudong Huang;Zhao Kang;Ivor W. Tsang;Zenglin Xu

  • Auto-weighted multi-view clustering via deep matrix decomposition

    Shudong Huang;Zhao Kang;Zenglin Xu

  • Multi-view Attributed Graph Clustering

    Zhiping Lin;Zhao Kang;Lizong Zhang;Ling Tian

  • Low-rank Kernel Learning for Graph-based Clustering

    Zhao Kang;Liangjian Wen;Wenyu Chen;Zenglin Xu

  • Structured graph learning for clustering and semi-supervised classification

    Zhao Kang;Chong Peng;Qiang Cheng;Xinwang Liu

  • Robust deep k-means: An effective and simple method for data clustering

    Shudong Huang;Zhao Kang;Zenglin Xu;Zenglin Xu;Quanhui Liu

  • Kernel-driven Similarity Learning

    Zhao Kang;Zhao Kang;Chong Peng;Qiang Cheng

  • Top-N recommender system via matrix completion

    Zhao Kang;Chong Peng;Qiang Cheng

  • Robust PCA Via Nonconvex Rank Approximation

    Zhao Kang;Chong Peng;Qiang Cheng

  • Multi-view subspace clustering via partition fusion

    Juncheng Lv;Zhao Kang;Boyu Wang;Luping Ji

  • Unified Spectral Clustering With Optimal Graph

    Zhao Kang;Chong Peng;Qiang Cheng;Zenglin Xu

  • Twin Learning for Similarity and Clustering: A Unified Kernel Approach.

    Zhao Kang;Chong Peng;Qiang Cheng

  • Auto-weighted multi-view co-clustering with bipartite graphs

    Shudong Huang;Zenglin Xu;Ivor W. Tsang;Zhao Kang

  • Graph Filter-based Multi-view Attributed Graph Clustering

    Zhiping Lin;Zhao Kang

  • Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification

    Zhao Kang;Xiao Lu;Jinfeng Yi;Zenglin Xu

  • Top-N Recommender System via Matrix Completion

    Zhao Kang;Chong Peng;Qiang Cheng

  • Robust Graph Regularized Nonnegative Matrix Factorization for Clustering

    Chong Peng;Zhao Kang;Yunhong Hu;Jie Cheng

Frequent Co-Authors

Zenglin Xu
Zenglin Xu Harbin Institute of Technology
Xi Peng
Xi Peng Sichuan University
Joey Tianyi Zhou
Joey Tianyi Zhou Agency for Science, Technology and Research
Joel N. Ullom
Joel N. Ullom National Institute of Standards and Technology
Kent D. Irwin
Kent D. Irwin Stanford University
Ivor W. Tsang
Ivor W. Tsang Agency for Science, Technology and Research
Jianke Zhu
Jianke Zhu Zhejiang University
Jinfeng Yi
Jinfeng Yi IBM (United States)
Xinwang Liu
Xinwang Liu National University of Defense Technology
Gene C. Hilton
Gene C. Hilton National Institute of Standards and Technology

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