Prof. CHEUNG, Yiu Ming
Prof. CHEUNG, Yiu Ming

張曉明教授
B.Sc., M.Phil., Ph.D., FIEEE, FIET, FBCS, FRSA, DFIETI, DFIDSAI
Professor, Department of Computer Science
http://www.comp.hkbu.edu.hk/~ymc
 

About

Prof. Cheung received the PhD degree from the Department of Computer Science and Engineering at the Chinese University of Hong Kong. Currently, he is a Full Professor at the Computer Science Department in Hong Kong Baptist University (HKBU). Also, he is serving as an Associate Editor in IEEE Transactions on Cybernetics, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Transactions on Cognitive and Developmental Systems, Pattern Recognition, and Neurocomputing, to name a few. Prof. Cheung is a recipient of IEEE CIS Distinguished Lecturer (Year 2020-2022), and the Chair of the IEEE Computer Society Technical Committee on Intelligent Informatics (TCII). 


Research Interests

  • Machine Learning
  • Data Science
  • Computer Vision
  • Pattern Recognition
  • Multi-objective Optimization
  • Information Hiding

Selected Publications

 
  1. Y.Q. Zhang and Y.M. Cheung*, “Learnable Weighting of Intra-attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes”, IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI:10.1109/TPAMI.2021.3056510.
  2. X. Liu, K. Hu, H.B. Ling and Y.M. Cheung*, “MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3): 964-981, 2021.
  3. Y. Zhou and Y.M. Cheung*, “Bayesian Low-Tubal-Rank Robust Tensor Factorization with Multi-Rank Determination”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1): 62-76, 2021.
  4. J. Lou and Y.M. Cheung*, “Robust Low-rank Tensor Minimization via a New Tensor Spectral k-Support Norm”, IEEE Transactions on Image Processing, 29(1): 2314-2327, 2020.
  5. Y. Lu, Y.M. Cheung* and Y.Y. Tang, “Bayes Imbalance Impact Index: A Measure of Class Imbalanced Dataset for Classification Problem”, IEEE Transactions on Neural Networks and Learning Systems, 31(9): 3525-3539, 2020.
  6. Y.M. Cheung*, F.Q. Gu, H.L. Liu, K.C. Tan and H. Huang, “Objective-Domain Dual Decomposition: An Effective Approach to Optimizing Partial Differentiable Objective Functions”, IEEE Transactions on Cybernetics, 50(3): 923-934, 2020.
  7. Y.M. Cheung* and Y.Q. Zhang, “Fast and Accurate Hierarchical Clustering Based on Growing Multi-Layer Topology Training”, IEEE Transactions on Neural Networks and Learning Systems, 30(3): 876-890, 2019.
  8. X. Liu and Y.M. Cheung*, "Learning Multi-Boosted HMMs for Lip-Password Based Speaker Verification", IEEE Transactions on Information Forensics and Security, 9(2): 233-246, 2014.
  9. H. Zeng and Y.M. Cheung*, “Feature Selection and Kernel Learning for Local Learning Based Clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8): 1532-1547, 2011.
  10. Y.M. Cheung, “Maximum Weighted Likelihood via Rival Penalized EM for Density Mixture Clustering with Automatic Model Selection”, IEEE Transactions on Knowledge and Data Engineering, 17(6): 750-761, 2005.

(Note: * Corresponding Author)