Peng Zhou

Peng Zhou

Peng Zhou (周芃)’s Homepage

I received the B.E. degree in computer science and technology from University of Science and Technology of China in 2011, and Ph.D degree in Computer Science from Institute of Software, Chinese Academy of Sciences in 2017. I am currently an Associate Professor in Anhui University. My research interests include clustering ensemble, multi-view learning, feature selection and so on. I have published 50+ papers in highly regarded conferences and journals, including NeurIPS, IJCAI, AAAI, ACM MM, SDM, ICDM, IEEE TKDE, IEEE TNNLS, IEEE TCYB, ACM TKDD, PR, etc. I have served as Program Committee for NeurIPS, ICLR, ICML, IJCAI, AAAI, ACM MM and reviewer for IEEE TPAMI, IEEE TKDE, IEEE TNNLS, IEEE T-CYB, etc.

What’s News

  • Oct 29, 2024 “Clustering Ensemble Based on Fuzzy Matrix Self-Enhancement” has been accepted by TKDE.
  • Sep 26, 2024 “Fair Kernel K-Means: from Single Kernel towards Multiple Kernel” has been accepted by NeurIPS 2024.
  • Jul 16, 2024 Three papers “One-Stage Fair Multi-View Spectral Clustering”, “Fast and Scalable Incomplete Multi-View Clustering with Duality Optimal Graph Filtering”, and “Scalable Multi-view Unsupervised Feature Selection with Structure Learning and Fusion” have been accepted by ACM MM 2024.
  • Apr 26, 2024 “Feature Graph Augmented Network Representation for Community Detection” has been accepted by IEEE TCSS.
  • Apr 17，2024 Two papers “Active Deep Multi-view Clustering” and “Efficient Multi-view Unsupervised Feature Selection with Adaptive Structure Learning and Inference” have been accepted by IJCAI 2024.
  • Mar 13, 2024 Two papers “Deep Self-paced Active Learning for Image Clustering” and “Jointly Learn the Base Clustering and Ensemble for Deep Image Clustering” have been accpeted by ICME 2024.
  • Jan 30, 2024 “Fair Feature Selection: A Causal Perspective” has been accepted by ACM TKDD.
  • Dec 27, 2023 “Multiple Kernel Clustering with Local Kernel Reconstruction and Global Heat Diffusion” has been accepted by Information Fusion.
  • Dec 12, 2023 Two papers “Higher Order Multiple Graph Filtering for Structured Graph Learning” and “Kmeans Clustering Based on Chebyshev Polynomial Graph Filtering” have been accepted by ICASSP 2024.
  • Nov 26, 2023 “Multi-view Clustering Based on Adaptive Similarity Graph Joint Optimization” has been accepted by Chinese Journal of Computers (in Chinese). (“自适应相似图联合优化的多视图聚类”被《计算机学报》接收。)
  • Oct 1, 2023 “Partial Clustering Ensemble” has been accepted by TKDE.
  • Sep 5, 2023 “Multi-view Outlier Detection via Graphs Denoising” has been accepted by Information Fusion.
  • Aug 26, 2023 “Clustering Ensemble via Diffusion on Adaptive Multiplex” has been accepted by TKDE.
  • Jul 26, 2023 “Learnable Graph Filter for Multi-view Clustering” has been accepted by ACM MM 2023.
  • Jul 3, 2023 “Bi-level Ensemble Method for Unsupervised Feature Selection” has been accepted by Information Fusion.
  • Apr 2, 2023 “Adaptive Consensus Clustering for Multiple K-means via Base Results Refining” has been accepted by TKDE.
  • Mar 13, 2023 Two papers “Medical Image Super-resolution via Diagnosis-guided Attention” and “INCLR: Intensifying the Consistency of Pseudo Label Refinement for Unsupervised Domain Adaption Person Re-Identification” have been accepted by ICME 2023.
  • Mar 1, 2023 “Active Clustering Ensemble with Self-paced Learning” has been accepted by IEEE TNNLS.
  • Nov 16, 2022 “A Node Classification-Based Multi-Objective Evolutionary Algorithm for Community Detection in Complex Networks” has been accepted by IEEE TCSS.
  • Oct 30, 2022 “A Light Causal Feature Selection Approach to High-Dimensional Data” has been accepted by IEEE TKDE.
  • Sep 20, 2022 “Self-paced Adaptive Bipartite Graph Learning for Consensus Clustering” has been accepted by ACM TKDD.
  • Jun 25, 2022 “Active Deep Image Clustering” has been accepted by Knowledge-Based Systems.
  • Mar 15, 2022 “Balanced Spectral Feature Selection” has been accepted by IEEE Transactions on Cybernetics.
  • Mar 6，2022 “FRATCF:Feature-Residue Real-Time UAV Tracking Based on Automatic Spatio-Temporal Regularization Correlation Filter” has been accepted by ICME 2022.

Researches

  • Clustering Ensemble
    • Chen Liang, Zhiqian Dong, Sheng Yang, Peng Zhou (Corresponding author), Jointly Learn the Base Clustering and Ensemble for Deep Image Clustering, IEEE Conference on Multimedia Expo, 2024, Accepted.
    • Peng Zhou, Liang Du, Xinwang Liu, Zhaolong Ling, Xia Ji, Xuejun Li, Yi-Dong Shen, Partial Clustering Ensemble, IEEE Transactions on Knowledge and Data Engineering, 2024,36(5):2096-2109.
    • Peng Zhou, Boao Hu, Dengcheng Yan, Liang Du, Clustering Ensemble via Diffusion on Adaptive Multiplex, IEEE Transactions on Knowledge and Data Engineering, 2024,36(4):1463-1474.
    • Peng Zhou, Liang Du, Xuejun Li, Adaptive Consensus Clustering for Multiple K-means via Base Results Refining, IEEE Transactions on Knowledge and Data Engineering, 2023,35(10):10251-10264.
    • Peng Zhou, Bicheng Sun, Xinwang Liu, Liang Du, Xuejun Li, Active Clustering Ensemble with Self-paced Learning, IEEE Transactions on Neural Networks and Learning Systems,2023, Accepted.
    • Peng Zhou, Xinwang Liu, Liang Du, Xuejun Li, Self-paced Adaptive Bipartite Graph Learning for Consensus Clustering, ACM Transactions on Knowledge Discovery from Data, 2023, 17, 5, Article 62:1-35.
    • Peng Zhou, Xia Wang, Liang Du, Xuejun Li, Clustering Ensemble via Structured Hypergraph Learning. Information Fusion, 2022, 78:171-179.
    • Peng Zhou, Liang Du, Yi-Dong Shen, Xuejun Li (2021) Tri-level Robust Clustering Ensemble with Multiple Graph Learning. The 35th AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, 2021:11125-11133.
    • Peng Zhou, Liang Du, Xinwang Liu, Yi-Dong Shen, Mingyu Fan, Xuejun Li, Self-paced Clustering Ensemble. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(4):1497-1511.
    • Peng Zhou, Liang Du, Xuejun Li (2020) Self-paced Consensus Clustering with Bipartite Graph. The 29th International Joint Conference on Artificial Intelligence (IJCAI), Yokohama, Japan, 2020:2133-2139.
    • Peng Zhou, Liang Du, Hanmo Wang, Lei Shi and Yi-Dong Shen. Learning a Robust Consensus Matrix for Clustering Ensemble via Kullback-Leibler Divergence Minimization. IJCAI-15 , 2015: 4112-4118.
  • Multi-view Learning
    • Rongwen Li, Haiyang Hu, Liang Du, Jiarong Chen, Bingbing Jiang, Peng Zhou (Corresponding author). One-Stage Fair Multi-View Spectral Clustering, ACM International Conference on Multimedia (ACM MM 2024), 2024, Accepted.
    • Helin Zhao, Wei Chen, Peng Zhou (Corresponding author). Active Deep Multi-view Clustering, The 33th International Joint Conference on Artificial Intelligence (IJCAI), South Korea, 2024, Accepted.
    • Boao Hu, Xu Wang, Peng Zhou (Corresponding author), Liang Du. Multi-view Outlier Detection via Graphs Denoising, Information Fusion, 2024,101,102012.
    • Peng Zhou, Liang Du. Learnable Graph Filter for Multi-view Clustering. ACM International Conference on Multimedia (ACM MM 2023), 2023, Accepted.
    • Peng Zhou, Yi-Dong Shen, Liang Du and Fan Ye. Incremental Multi-view Support Vector Machine. The Nineteenth SIAM International Conference on Data Mining (SDM-19), Calgary, Canada, May 2019.
    • Peng Zhou, Yi-Dong Shen, Liang Du, Fan Ye, Xuejun Li, Incremental multi-view spectral clustering. Knowledge-Based System, 174, 2019:73-86.
    • Peng Zhou, Liang Du, Mingyu Fan and Yi-Dong Shen. An LLE based Heterogeneous Metric Learning for Crossmedia Retrieval. The Fifteenth SIAM International Conference on Data Mining (SDM-15), 2015: 64-72.
  • Feature Selection
    • Zhaolong Ling, Enqi Xu, Peng Zhou (Corresponding author), Liang Du, Kui Yu, Xindong Wu, Fair Feature Selection: A Causal Perspective, ACM Transactions on Knowledge Discovery from Data, 2024, Accepted.
    • Peng Zhou, Xia Wang, Liang Du, Bi-level Ensemble Method for Unsupervised Feature Selection, Information Fusion, 2023,100,101910.
    • Zhaolong Ling, Ying Li, Yiwen Zhang, Kui Yu, Peng Zhou, Bo Li, Xindong Wu, A Light Causal Feature Selection Approach to High-Dimensional Data, IEEE Transactions on Knowledge and Data Engineering, 2023, 35(8):7639-7650.
    • Peng Zhou, Jiangyong Chen, Liang Du, Xuejun Li, Balanced Spectral Feature Selection, IEEE Transactions on Cybernetics, 2023, 53(7):4232-4244.
    • Xiaoqin zhang, Mingyu Fan, Di Wang, Peng Zhou (Corresponding author), Dacheng Tao, Top-k Feature Selection Framework using Robust 0-1 Integer Programming, IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(7):3005-3019. (ESI Highly Cited Paper, ESI Hot Cited Paper)
    • Peng Zhou, Liang Du, Xuejun Li, Yi-Dong Shen, Yuhua Qian, Unsupervised Feature Selection with Adaptive Multiple Graph Learning, Pattern Recognition, 105, 2020, 107375.
    • Peng Zhou, Jiangyong Chen, Mingyu Fan, Liang Du, Yi-Dong Shen, Xuejun Li, Unsupervised feature selection for balanced clustering. Knowledge-Based System, 193,2020, 105417.
    • Liang Du, Zhiyong Shen, Xuan Li, Peng Zhou and Yi-Dong Shen. Local and Global Discriminative Learning for Unsupervised Feature Selection. The Thirteenth IEEE International Conference on Data Mining (ICDM-13), 2013:131-140.
  • Active Learning
    • Helin Zhao, Wei Chen, Peng Zhou (Corresponding author). Active Deep Multi-view Clustering, The 33th International Joint Conference on Artificial Intelligence (IJCAI), South Korea, 2024, Accepted.
    • Helin Zhao, Wei Chen, Peng Zhou (Corresponding author), Deep Self-paced Active Learning for Image Clustering, IEEE Conference on Multimedia Expo, 2024, Accepted.
    • Peng Zhou, Bicheng Sun, Xinwang Liu, Liang Du, Xuejun Li, Active Clustering Ensemble with Self-paced Learning, IEEE Transactions on Neural Networks and Learning Systems,2023, Accepted.
    • Bicheng Sun, Peng Zhou (Corresponding author), Liang Du, Xuejun Li. Active Deep Image Clustering. Knowledge-Based Systems, 2022,252,109346.
    • Hanmo Wang, Liang Du, Peng Zhou, Lei Shi, Yi-Dong Shen. Convex batch mode active sampling via α-relative pearson divergence. Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15). 2015: 3045-3051.
    • Hanmo Wang, Liang Du, Lei Shi, Peng Zhou, Yuhua Qian, and Yi-Dong Shen. Experimental Design With Multiple Kernels. The Fifteenth IEEE International Conference on Data Mining (ICDM-15), 2015: 419-428.
  • Multiple Kernel Learning
    • Peng Zhou, Rongwen Li, Liang Du. Fair Kernel K-Means: from Single Kernel towards Multiple Kernel. The Thirty-eighth Annual Conference on Neural Infomration Processing Systems (NeurIPS 2024), 2024, Accepted.
    • Yan Chen, Liang Du, Peng Zhou, Lei Duan, Yuhua Qian. Multiple Kernel Clustering with Local Kernel Reconstruction and Global Heat Diffusion. Information Fusion, 2024,105:102219.
    • Peng Zhou, Liang Du, Lei Shi, Hanmo Wang and Yi-Dong Shen. Recovery of Corrupted Multiple Kernels for Clustering. The Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-15), 2015: 4105-4111.
    • Liang Du, Peng Zhou, Lei Shi, Hanmo Wang, Mingyu Fan, Wenjian Wang and Yi-Dong Shen. Robust Multiple Kernel K-means using L2-1-norm. The Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-15), 2015: 3476-3482.
    • Hanmo Wang, Liang Du, Lei Shi, Peng Zhou, Yuhua Qian, and Yi-Dong Shen. Experimental Design With Multiple Kernels. The Fifteenth IEEE International Conference on Data Mining (ICDM-15), 2015: 419-428.