
CHEUNG, Yiuming (張曉明), PhD, IEEE Fellow,
IET Fellow,

Profile:
Yiuming
Cheung is a Full Professor
of Department of Computer Science and an
Associate Director of Institute
of Computational and Theoretical Studies at Hong Kong Baptist
University (HKBU). He received PhD degree from Department of Computer
Science and Engineering at The Chinese University of Hong Kong in 2000, and
then joined the Department of Computer Science at HKBU in 2001. He is an IEEE Fellow, IET/IEE Fellow,
British Computer Society (BCS) Fellow, Fellow of the Royal Society of Arts
(RSA), and Distinguished Fellow of International Engineering and Technology
Institute, Hong Kong (IETI).
His
research interests include Artificial
Intelligence, Intelligent Visual Computing, Pattern Recognition, Data
Mining, Watermarking, and Optimization. He
has published over 250
articles in the highquality conferences and journals, including IEEE
Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on
Neural Networks and Learning Systems, IEEE Transactions on Information
Forensics and Security, IEEE Transactions on Image Processing, IEEE
Transactions on Knowledge and Data Engineering, IEEE Transactions on SMC (Part
B), Pattern Recognition, and so on. His
coauthored work on multimodal optimization won the championship in the
competition of MultiNiche Optimization held in CEC’2015  a flagship
international conference on Evolutionary Computation. Moreover, he has been
granted one Chinese patent and two US patents. In particular, selected from
1000 new inventions and products of 700+ competition teams from 40 countries,
he was awarded two most prestigious prizes: (1) the Gold Medal with Distinction
(i.e. the highest grade in Gold Medals) and (2) Swiss Automobile Club Prize, in
the 45^{th} International Exhibition of Invention, Geneva, Switzerland,
on March 29April 2, 2017, in recognition of his innovative work. Also, he was
the Gold Award Winner of Hong Kong Innovative Invention Award in the Seventh
Hong Kong Innovative Technologies Achievement Award 2017. In addition, he won
the Gold Medal with Congratulations of Jury (i.e. the highest grade in Gold
Medals) and the Award of Excellence from Romania, respectively, at the 46th
International Exhibition of Inventions of Geneva 2018 with his invention
“Lippassword: Double Security System for Identity Authentication”. Prof.
Cheung
was the
recipient of an IEEE Distinguished Lecturer (20202022), 2011 Best Research
Award in Department of Computer Science, HKBU, the recipient
of Best
Paper Awards in SEAL’2017, ISICA’2017, ICNCFSKD’2014, and IEEE
IWDVT’2005, respectively, the recipient of
Best Student Paper Award in ISMIS’2018, and the recipient of 2017 IETI Annual
Scientific Award. He has been recognized as the recipient of 20172019 Albert
Nelson Marquis Lifetime Achievement Award.
He is
the Founding
Chairman of IEEE (Hong Kong) Computational Intelligence Chapter and the Chair of Technical
Committee on Intelligent Informatics (TCII) of IEEE Computer Society. He
has served in various capacities (e.g.,
Organizing Committee Chair, Program Committee Chair, Program Committee Area Chair, Financial
Chair, Senior
Program Committee Member, etc.) at several toptier international
conferences, including IEEEsponsored
IJCAIECAI’2018, ICDM’2017 & 2018, WCCI’2016, WIIAT’2012, ICDM’2006 & WIIAT’2006. Currently, he is the
Associate Editor of several
prestigious journals, including IEEE Transactions on Neural Networks and
Learning Systems, IEEE Transactions on Cybernetics, Pattern Recognition, Knowledge
and Information Systems (KAIS), International
Journal of Pattern Recognition and Artificial Intelligence, and Neurocomputing,
as
well as the Guest Coeditor
in
several international journals.
International
Software Competition:
Yiuming
Cheung, as the
lead author,
has proposed
a novel approach and developed software accordingly to participate in the
worldwide International Nonlinear Financial Forecasting Competition (INFFC),
held in USA in 1995. In this competition, all participated software was tested using reallife
financial data, and evaluated by more than 12 major performance evaluation
indices. The performance of their developed software is at either firstplace
or second place under the measurement of these major indices. The detailed
competition results, including the performance assessment of their software by
a set of indices, are both published in the Proceedings of the First INFFC,
edited by Randall B. Caldwell, Finance & Technology Publishing, 1997.
The List of Patents:
1.
“Method
and Apparatus for Eye Gaze Tracking ” [US Patent: US9,563,805];
2. “A Lippassword Based Speaker
Verification System” [US Patent: US9,159,321B2, Video: Mandarin, English];
3. "An InfraredSpectrum
Based Recognition System for Identifying Chinese Herbal Species, Origins and
Growth Mode with Highrecognition Rate" [Chinese Patent: ZL200810005068.3, Introduction].
I. Selected Refereed Journals (Sorted by Reverse
Chronological Order)
[J.066] M. Pang, Y.M. Cheung, Q.Q. Shi and M.K. Li,
“Iterative Dynamic Generic Learning for Face Recognition from a Contaminated
Single Sample per Person”, IEEE
Transactions on Neural Networks and Learning Systems, DOI:
10.1109/TNNLS.2020.2985099.
[J.065] Y.Q.
Zhang and Y.M. Cheung, “A New
Distance Metric Exploiting Heterogeneous InterAttribute Relationship for
OrdinalandNominalAttribute Data Clustering”, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2020.2983073 [Source Code].
[J.064] Y.
Lu, Y.M. Cheung and Y.Y. Tang,
“Adaptive Chunkbased Dynamic Weighted Majority for Imbalanced Data Streams
with Concept Drift”, IEEE Transactions on
Neural Networks and Learning Systems, DOI:
10.1109/TNNLS.2019.2951814
[Source Code].
[J.063] J. Lou
and Y.M. Cheung, “Robust Lowrank Tensor Minimization
via a New Tensor Spectral kSupport
Norm”, IEEE Transactions on Image
Processing, Vol. 29, No. 1, pp. 23142327, 2020, DOI:
10.1109/TIP.2019.2946445 [Supplement].
[J.062] 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, DOI:
10.1109/TNNLS.2019.2944962 [Source
Code].
[J.061] X. Liu,
Z.K. Hu, H.B. Ling and Y.M. Cheung, “MTFH:
A Matrix TriFactorization Hashing Framework for Efficient CrossModal
Retrieval”, IEEE Transactions on Pattern
Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2019.2940446 [Source Code].
[J.060] S.Y.
Yi, Z.Y. He, X.Y. Jing, Y. Li, Y.M.
Cheung and F.P. Nie, “Adaptive
Weighted Sparse Principal Component Analysis for Robust Unsupervised Feature
Selection”, IEEE Transactions on
Neural Networks and Learning Systems, Vol. 31, No. 6, pp. 21532163, June
2020 [DOI:10.1109/TNNLS.2019.2928755].
[J.059] Y. Zhou and Y.M. Cheung, “Bayesian LowTubalRank
Robust Tensor Factorization with MultiRank Determination”, IEEE Transactions on Pattern Analysis and
Machine Intelligence, DOI:10.1109/TPAMI.2019.2923240 [Source Code].
[J.058] M. Pang, Y.M. Cheung, B.H. Wang and J. Lou, “Synergistic Generic Learning for Face
Recognition from a Contaminated Single Sample per Person”, IEEE Transactions on Information Forensics
and Security, Vol. 15, pp. 195209, 2020, [DOI:10.1109/TIFS.2019.2919950, Demo Code].
[J.057] Q.M. Peng and Y.M. Cheung, “Automatic Video Object Segmentation
Based on Visual and Motion Saliency”, IEEE
Transactions on Multimedia, Vol. 21, No. 12, pp. 30833094, December, 2019
[DOI:10.1109/TMM.2019.2918730].
[J.056] Y. Lu, Y.M. Cheung and Y.Y. Tang,
“SelfAdaptive MultiPrototypebased Competitive Learning Approach: A
kmeanstype Algorithm for Imbalanced Data Clustering”, IEEE Transactions on Cybernetics, DOI:10.1109/TCYB.2019.2916196 [Source Code].
[J.055] Y. Zhou, H.P. Lu and Y.M. Cheung, “Probabilistic RankOne
Tensor Analysis with Concurrent Regularizations”, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2019.2914316 [Source Code].
[J.054] Y.Q. Zhang, Y.M. Cheung and K.C. Tan, “A Unified EntropyBased Distance
Metric for OrdinalandNominalAttribute Data Clustering”, IEEE Transactions on Neural Networks and
Learning Systems, Vol. 31, No. 1, pp. 3952, January 2020, DOI:
10.1109/TNNLS.2019.2899381 [Source Code].
[J.053] M. Pang, Y.M. Cheung, B.H. Wang and R.S. Liu, “Robust Heterogeneous Discriminative
Analysis for Face Recognition with Single Sample per Person”, Pattern Recognition, Vol. 89, pp.
91107, 2019 [DOI:
10.1016/j.patcog.2019.01.005, Source Code].
[J.052] X. Liu, J.J. Geng, H.B.
Ling and Y.M. Cheung, “Attention Guided
Deep Audioface Fusion for Efficient Speaker Naming”, Pattern Recognition, Vol. 88, pp. 557568, 2018.
[J.051] M. Pang, Y.M. Cheung, R.S. Liu, J. Lou and C.
Lin, “Toward Efficient
Image Representation: Sparse Concept Discriminant Matrix Factorization”, IEEE Transactions on Circuits and Systems
for Video Technology, Vol. 29, No. 11, pp. 31843198, November 2019 [DOI:
10.1109/TCSVT.2018.2879833, Source Code].
[J.050] S.Y. Yi, Z.Y. He, Y. Li
and Y.M. Cheung, “Dual Pursuit for Subspace
Learning”, IEEE Transactions on
Multimedia, Vol. 21, No. 6, pp. 13991411, June 2019 [DOI:10.1109/TMM.2018.2877888].
[J.049] Q.Q. Shi, Y.M. Cheung, Q.B. Zhao and H.P. Lu, “Feature Extraction for
Incomplete Data via Lowrank Tensor Decomposition with Feature Regularization”,
IEEE Transactions on Neural Networks and
Learning Systems, Vol. 30, No. 6, pp. 18031817, June 2019
[DOI:10.1109/TNNLS.2018.2873655].
[J.048] Y.M. Cheung, F.Q. Gu, H.L. Liu, K.C. Tan and H. Huang, “ObjectiveDomain Dual
Decomposition: An Effective Approach to Optimizing Partial Differentiable
Objective Functions”,
IEEE Transactions on Cybernetics,
Vol. 50, No. 3, pp. 923934, March 2020 [DOI: 10.1109/TCYB.2018.2870487].
[J.047] Y. Zhou and Y.M. Cheung, “Probabilistic
RankOne Discriminant Analysis via Collective and Individual Variation Modeling”,
IEEE Transactions on Cybernetics,
Vol. 50, No. 2, pp. 627639, February 2020 [DOI:10.1109/TCYB.2018.2870440].
[J.046] L.
Chen, H.L. Liu, K.C. Tan, Y.M. Cheung
and Y.P. Wang, “Evolutionary
ManyObjective Algorithm Using Decomposition Based Dominance Relationship”,
IEEE Transactions on Cybernetics,
Vol. 49, No. 12, pp. 41294139, December, 2019 [DOI: 10.1109/TCYB.2018.2859171].
[J.045] Y.M. Cheung and Y.Q. Zhang, “Fast
and Accurate Hierarchical Clustering Based on Growing MultiLayer Topology
Training”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, No. 3, pp. 876890, March 2019 [DOI: 10.1109/TNNLS.2018.2853407, Source Code].
[J.044] Q.Q. Shi, H.P. Lu and Y.M. Cheung, “RankOne Matrix
Completion with Automatic Rank Estimation via L1Norm Regularization”, IEEE Transactions on Neural Networks
and Learning Systems, 29(10):47444757, October 2018 [DOI:
10.1109/TNNLS.2017.2766160, Source Code].
[J.043] J.B. Zeng, Y. Liu, B. Leng, Z. Xiong and Y.M. Cheung, “Dimensionality
Reduction in Multiple Ordinal Regression”, IEEE Transactions on Neural Networks and Learning Systems,
29(9):40884101, September 2018 [DOI: 10.1109/TNNLS.2017.2752003].
[J.042] H. Jia and Y.M. Cheung, “Subspace
Clustering of Categorical and Numerical Data With an Unknown Number of Clusters”,
IEEE Transactions on Neural Networks and
Learning Systems, 29(8):33083325, August 2018 [DOI:
10.1109/TNNLS.2017.2728138, Source Code].
[J.041] F.Q. Gu and Y.M. Cheung, “Selforganizing
Mapbased Weight Design for Decompositionbased Manyobjective Evolutionary
Algorithm”, IEEE Transactions on
Evolutionary Computation, 22(2):211225, March 2018 [DOI: 10.1109/TEVC.2017.2695579].
[J.040] Y. Wang, J.W. Wan, J. Guo, Y.M.
Cheung and P.C. Yuen, “Inferencebased
Similarity Search in Randomized Montgomery Domains for PrivacyPreserving Biometric
Identification”, IEEE Transactions on
Pattern Analysis and Machine Intelligence, 40(7):16111624, July 2018 [DOI:
10.1109/TPAMI.2017.2727048].
[J.039] S.Y. Yi, Z.Y.
He, Y.M. Cheung and W.S. Chen, “Unified Sparse
Subspace Learning via Selfcontained Regression”, IEEE Transactions on Circuits and Systems for Video Technology,
28(10):25372550, October 2018 [DOI: 10.1109/TCSVT.2017.2721541].
[J.038] Y. Zhang, Y.M. Cheung,
B. Xu and W.F. Su, “Detection Copy
Number Variants from NGS with Sparse and Smooth Constraints”, IEEE/ACM Transactions on Computational Biology
and Bioinformatics, 14(4):856867, August 2017 [DOI:
10.1109/TCBB.2016.2561933].
[J.037] X. Liu, H. Zhang, Y.M. Cheung, X.G. You and Y.Y. Tang, “Efficient Single Image Dehazing and
Denosing: An Efficient Multiscale Correlated Wavelet Approach”, Computer Vision and Image Understanding,
Vol. 162, pp. 2333, 2017 [DOI: 10.1016/j.cviu.2017.08.002] [Source Code].
[J.036] X. Liu, G.F. He, S.J. Peng, Y.M.
Cheung and Y.Y. Tang, “Efficient Human
Motion Retrieval via Temporal Adjacent Bag of Words and Discriminative
Neighborhood Preserving Dictionary Learning”, IEEE Transactions on HumanMachine System, 47(6):763776, 2017 [DOI: 10.1109/THMS.2017.2675959].
[J.035] Y.M. Cheung and J.
Lou, “Proximal
Average Approximated Incremental Gradient Descent for Composite Penalty Regularized
Empirical Risk Minimization”, Machine
Learning, 106(4):595622, April 2017 [DOI: 10.1007/s1099401656091].
[J.034] Y.M. Cheung, M. Li, Q.M. Peng and
C.L. Philip Chen, “A Cooperative
Learningbased Clustering Approach to Lip Segmentation without Knowing Segment
Number”, IEEE Transactions on
Neural Networks and Learning Systems, 28(1):8093, January 2017 [DOI: 10.1109/TNNLS.2015.2501547].
[J.033] Z.Y. He, S.Y. Yi, Y.M. Cheung, X.G. You and Y.Y. Tang, “Robust Object
Tracking via Key Patch Sparse Representation”, IEEE Transactions on Cybernetics, 47(2):354364, February 2017
[DOI: 10.1109/TCYB.2016.2514714].
[J.032] S.Y. Yi, Z.H. Lai, Z.Y.
He, Y.M. Cheung and Y. Liu, “Joint Sparse
Principal Component Analysis”, Pattern
Recognition, Vol. 61, pp. 524536, 2017 [Source Code].
[J.031] Q.M. Peng, Y.M. Cheung,
X.G. You and Y.Y. Tang, “A Hybrid of Local
and Global Saliencies for Detecting Image Salient Region and Appearance”, IEEE Transactions on Systems, Man and
Cybernetics: Systems, 47(1):8697, January 2017 [DOI: 10.1109/TSMC.2016.2564922].
[J.030] Y.M. Cheung, F.Q. Gu and H.L. Liu, “Objective Extraction
for ManyObjective Optimization Problems: Algorithm and Test Problems”, IEEE Transactions on Evolutionary
Computation, 20(5): 755772, October 2016.
[J.029] H.
Jia, Y.M. Cheung and J.M. Liu, “A New Distance
Metric for Unsupervised Learning of Categorical Data”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 27, No. 5, pp. 10651079,
2016.
[J.028] X. Liu, Y.M. Cheung and Y.Y. Tang, “Lip Event Detection
Using Oriented Histograms of Regional Optical Flow and Low Rank Affinity
Pursuit”, Computer Vision and Image
Understanding, Vol. 148, pp. 153163, 2016 [DOI:
10.1016/j.cviu.2015.11.015].
[J.027] P.Y. Zhang, X.G. You, W.H. Ou, C.L.
Philip Chen and Y.M. Cheung, “Sparse Discriminative
Multimanifold Embedding for Onesample Face Identification”, Patter Recognition, Vol. 52, pp.
249259, April 2016.
[J.026] Z.Q. Zhao, Y.M.
Cheung, H.B. Hu, and X.D. Wu, “Corrupted and
Occluded Face Recognition via Cooperative Sparse Representation”, Pattern Recognition, Vol. 56, pp. 7787,
August 2016.
[J.025] Y.
Wang, L.P. Wang, Y.M. Cheung and
P.C. Yuen, “Learning
Compact Binary
Codes for Hashbased Fingerprint Indexing”, IEEE Transactions on Information Forensics and Security, 10(8), pp.
16031616, 2015.
[J.024] Y.M.
Cheung
and Q.M. Peng, “Eye
Gaze Tracking with a Web Camera in a Desktop Environment”, IEEE Transactions on HumanMachine Systems,
45(4): 419430, 2015.
[J.023] Y.M.
Cheung,
M. Li, X. Cao and X.G. You,
"Lip Segmentation under
MAPMRF Framework with Automatic Selection
of Local Observation Scale and Number of Segments", IEEE
Transactions on Image Processing, Vol. 23,
No. 8, pp. 33973411, 2014.
[J.022] X. Liu and Y.M. Cheung, "Learning
MultiBoosted HMMs for LipPassword Based Speaker Verification",
IEEE
Transactions on Information Forensics and Security, Vol.
9, No. 2, pp. 233246, 2014.
[J.021] H.Jia, Y.M.
Cheung and J. Liu, "Cooperative and Penalized Competitive
Learning with Application to Kernelbased Clustering", Pattern Recognition, Vol. 47, pp. 30603069, 2014.
[J.020] M.
Lutf, X.G. You, Y.M. Cheung and C.L.
Philip Chen, “Arabic
Font Recognition Based on Diacritics Features”, Pattern Recognition, Vol.
47, No. 2, pp. 672684, 2014.
[J.019]
Y.M.
Cheung and H. Jia, "Categoricalandnumericalattribute
Data Clustering based on a Unified Similarity Metric without Knowing Cluster
Number", Pattern Recognition,
Vol. 46, No. 8, pp. 22282238, 2013.
[Source Code (zip)]
[J.018] H.
Zeng and Y.M. Cheung, "Semisupervised Maximum
Margin Clustering with Pairwise Constraints", IEEE Transactions on
Knowledge and Data Engineering, Vol. 24, No. 5, pp. 926939, 2012.
[J.017]
Y.M.
Cheung,
X. Liu and X.G. You, "A Local Region Based
Approach to Lip Tracking", Pattern Recognition, Vol. 45, No. 9, pp. 33363347, 2012.
[J.016] H.
Zeng and Y.M. Cheung, "Feature
Selection and Kernel Learning for Local LearningBased Clustering", IEEE
Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 8,
pp. 15321547, August 2011.
[J.015] X.G.
You, L. Du, Y.M. Cheung and Q.H. Chen, "A Blind
Watermarking Scheme Using New Nontensor Product Wavelet Filter Banks",
IEEE Transactions on Image Processing, Vol. 19, No. 12, pp. 32713284,
2010.
[J.014] Y.
Liu, Mark Li, Y.M. Cheung, Pak C. Sham and Michael K. Ng, "SKMSNP: SNP
Markers Detection Method", Journal of Biomedical Informatics,
Vol. 43, No. 2, pp. 233239, 2010.
[J.013] H.T.
Wu and Y.M. Cheung, "Reversible
Watermarking by Modulation and Security Enhancement", IEEE
Transactions on Instrumentation and Measurement, Vol. 59, No. 1, pp.
221228, 2010.
[J.012]
Y.M.
Cheung
and H. Zeng, "Local
Kernel Regression Score for Selecting Features of Highdimensional Data",
IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 12,
pp. 17981802, 2009.
[J.011] H.
Zeng and Y.M. Cheung, "A New Feature
Selection Method for Gaussian Mixture Clustering", Pattern
Recognition, Vol. 42, No. 2, pp. 243250, 2009.
[J.010] Mark
J. Li, Michael K. Ng, Y.M. Cheung and Joshua Z.X. Huang, "Agglomerative Fuzzy
KMeans Clustering Algorithm with Selection of Number of Clusters", IEEE
Transactions on Knowledge and Data Engineering, Vol. 20, No. 11, pp.
15191534, 2008.
[J.009]
Y.M.
Cheung
and H.T. Wu, "A
Sequential Quantization Strategy for Data Embedding and Integrity
Verification", IEEE Transactions on Circuits and Systems for Video
Technology, Vol. 17, No. 8, pp. 10071016, 2007.
[J.008] Z.Y.
Zhang and Y.M. Cheung, "On Weight Design
of Maximum Weighted Likelihood and an Extended EM Algorithm", IEEE
Transactions on Knowledge and Data Engineering, Vol. 18, No. 10, pp.
14291434, 2006.
[J.007]
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, Vol. 17, No. 6, pp. 750761, 2005. [Source Code (zip)]
[J.006]
Y.M.
Cheung,
"On Rival
Penalization Controlled Competitive Learning for Clustering with Automatic
Cluster Number Selection", IEEE Transactions on Knowledge and Data
Engineering, Vol. 17, No. 11, pp. 15831588, 2005.
[J.005] X.M.
Zhao, Y.M. Cheung and D.S. Huang, "A Novel Approach to
Extracting Features from Motif Content and Protein Composition for Protein
Sequence Classification", Neural Networks, Vol. 18, No. 8, pp.
10191028, October, 2005.
[J.004]
Y.M.
Cheung ,
"k*Means: A
New Generalized kMeans Clustering Algorithm", Pattern Recognition
Letters, Vol. 24, Issue 15, pp. 28832893, 2003.
[J.003]
Y.M.
Cheung and
L. Xu, "Dual
Multivariate Autoregressive Modeling in State Space for Temporal Signal Separation",
IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics,
Vol. 33, No. 3, pp. 386398, June, 2003.
[J.002] K.K.
Hung, Y.M. Cheung and L. Xu, "An Extended ASLD Trading
System to Enhance Portfolio Management", IEEE Transactions on
Neural Networks, Vol. 14, No. 2, pp. 413425, March, 2003.
[J.001]
Y.M.
Cheung and
L. Xu, "An RPCLbased
Approach for Markov Model Identification with Unknown State
Number", IEEE Signal Processing Letters, Vol. 7, No. 10, pp.
284287, October, 2000.
II. Selected Refereed Conference Papers (Sorted by
Reverse Chronological Order)
[C.046] R. Wang, X. Liu, Y.M.
Cheung, K. Cheng, N.N. Wang and W.T. Fan, “Learning Discriminative Joint
Embeddings for Efficient Face and Voice Association”, Proceedings of the 43^{rd} International ACM SIGIR Conference
on Research and Development in Information Retrieval (SIGIR’20), July
2530, 2020, in press.
[C.045] Z.H. Guan, H.T. Wu and Y.M.
Cheung, “A Reversible Contrast Enhancement Scheme for Color Images”, Proceedings of 2020 IEEE International
Conference on Multimedia & Expo (ICME’2020), London, UK, July 610,
2020, in press.
[C.044] M. Pang, Y.M. Cheung,
Q.Q. Shi and M.K. Li, “Iterative Dynamic Generic Learning for Single Sample Face
Recognition with a Contaminated Gallery”, Proceedings
of 2020 IEEE International Conference on Multimedia & Expo (ICME’2020),
London, UK, July 610, 2020, in press.
[C.043] Y.Q. Zhang and Y.M.
Cheung, “An Ordinal Data Clustering Algorithm with Automated Distance
Learning”, Proceedings of the
Thirtyfourth AAAI Conference on Artificial Intelligence (AAAI’2020), New
York, USA, February 712, 2020, in press.
[C.042] Z.K. Hu, X. Liu, X.Z. Wang, Y.M.
Cheung, N.N. Wang and Y.W. Chen, “Triplet Fusion Network Hashing for
Unpaired Crossmodel Retrieval”, Proceedings of the 2019 on International
Conference on Multimedia Retrieval (ICMR’2019), pp. 141149, Ottawa ON, Canada,
June 1013, 2019 [Paper].
[C.041] X.Z. Wang, X. Liu, S.J. Peng, Yiuming Cheung, Z.K. Hu and N.N. Wang, “Fast Semantic Preserving
Hashing for LargeScale CrossModal Retrieval”, Proceedings of the 19th IEEE International Conference on Data Mining
(ICDM’2019), pp. 13481353, Beijing, China, November 811, 2019 [Paper].
[C.040] J. Lou and Y.M. Cheung,
“Uplink Communication Efficient Differentially Private Sparse Optimization with
Featurewise Distributed Data”, Proceedings
of the Thirtysecond AAAI Conference on Artificial Intelligence
(AAAI’2018), pp. 125133, New Orleans, Louisiana, USA, February 27, 2018 [Paper].
[C.039] Y. Liu, Z.L. Gu, Y.M.
Cheung and K.A. Hua, “Multiview Manifold Learning for Media
Interestingness Prediction”, Proceedings
of ACM International Conference on Multimedia Retrieval (ICMR’2017), pp.
308314, Bucharest, Romania, June 69, 2017 [Paper].
[C.038] M. Pang, Y.M. Cheung, B.H. Wang and R.S. Liu,
“Robust Heterogeneous Discriminative Analysis for Single Sample Per Person Face
Recognition”, Proceedings of the 26^{th}
ACM International Conference on Information and Knowledge Management
(CIKM’17), pp. 22512254, Singapore, November 610, 2017. [Paper]
[C.037] Q.Q. Shi,
H.P. Lu and Y.M. Cheung, “Tensor
Rank Estimation and Completion via CPbased Nuclear Norm”, Proceedings of the 26^{th} ACM International Conference on
Information and Knowledge Management (CIKM’17), pp. 949958, Singapore,
November 610, 2017. [Paper,
Source Code (zip)]
[C.036] Q.Q. Shi, Y.M. Cheung and Q.B. Zhao, “Feature
Extraction for Incomplete Data via Lowrank Tucker Decomposition”, Proceedings of the European Conference on
Machine Learning and Principles and Practice of Knowledge Discovery in
Databases (ECMLPKDD’2017), pp. 564581, Skopje, Macedonia, September
1822, 2017. [Paper,
Source Code (zip)]
[C.035] Y. Lu, Y.M. Cheung and Y.Y. Tang, “Dynamic
Weighted Majority for Incremental Learning of Imbalanced Data Streams with
Concept Drift”, Proceedings of the 26^{th}
International Joint Conference on Artificial Intelligence (IJCAI’2017), pp.
23932399, Melbourne, Australia, August 1925, 2017. [Paper, Source Code (zip)]
[C.034] Y. Zhou, H.P. Lu and Y.M.
Cheung, “Bilinear Probabilistic Canonical Correlation Analysis via Hybrid
Concatenations”, Proceedings of the
Thirtyfirst AAAI Conference on Artificial Intelligence (AAAI’2017), pp. 29492955, San
Francisco, California USA, February 49, 2017. [Paper]
[C.032] Y.M. Cheung and J.
Lou, “Scalable Spectral kSupport Norm Regularization for Robust Low Rank
Subspace Learning”, Proceedings of the 25^{th}
ACM International Conference on Information and Knowledge Management
(CIKM’2016), pp. 11511160, Indianapolis, USA, October 2428, 2016, DOI: http://dx.doi.org/10.1145/2983323.2983738 [Paper, Experimental Code].
[C.031] H.F. Huang, H. Zhang and Y.M.
Cheung, “The Common SelfPolar Triangle of Separate Circles: Properties and
Applications to Camera Calibration”, Proceedings
of IEEE International Conference on Image Processing (ICIP’2016), pp.
11701174, Phoenix, Arizona, USA, September 2528, 2016. [Paper]
[C.030] Y. Lu, Y.M.
Cheung and Y.Y. Tang, “Hybrid Sampling with Bagging for Class Imbalance
Learning”, Proceedings of the 20^{th}
PacificAsia Conference on Knowledge Discovery and Data Mining (PAKDD’2016),
LNAI 9651, Part I, pp. 1426, Auckland,
New Zealand, April 1922, 2016. [Paper]
[C.029] H.F. Huang, H. Zhang and Y.M.
Cheung, “Homography Estimation from the Common Selfpolar Triangle of
Separate Ellipses”, Proceedings of 2016 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR’2016), pp. 17371744, 2016. [Paper]
[C.028] Y.M. Cheung
and J. Lou, “Efficient Generalized Conditional Gradient with Gradient Sliding
for Composite Optimization”, Proceedings
of the TwentyFourth International Joint Conference on Artificial Intelligence
(IJCAI’2015), pp. 34093415, 2015.
[Paper]
[C.027] H.F. Huang, H. Zhang and Y.M. Cheung, “The Common Selfpolar Triangle of Concentric Circles
and Its Application to Camera Calibration”, Proceedings
of the IEEE Conference on Computer Vision and Pattern Recognition
(CVPR’2015), pp. 40654072, 2015.
[Paper]
[C.026] Y.M. Cheung
and J. Lou, “Proximal Average Approximated Incremental Gradient Method for
Composite Penalty Regularized Empirical Risk Minimization”, JMLR: Workshop and Conference Proceedings of the 7^{th} Asian
Conference on Machine Learning (ACML’2015), 45, pp. 205220, Hong Kong,
November 2022, 2015.
[Paper]
[C.025] Z.Q. Zhao, B.J. Xie, Y.M. Cheung and X.D. Wu, “Plant Leaf Identification via
A Growing Convolution Neural Network with Progressive Sample Learning”, Proceedings of the 12^{th} Asian Conference on Computer Vision
(ACCV’14), Part II, LNCS 9004, pp. 348361, Singapore, November 15, 2014.
[C.024] H.F. Huang, H. Zhang and Y.M. Cheung, “Camera Calibration Based on the Common Selfpolar
Triangle of Sphere Images”, Proceedings
of the 12^{th} Asian Conference on Computer Vision (ACCV’14), Part
II, LNCS 9004, pp. 1929, Singapore, November 15, 2014. [Paper]
[C.023] J.
Wang, Z.Q. Zhao, X.G. Hu, Y.M. Cheung,
M. Wang and X.D. Wu, “Online Group Feature Selection”, Proceedings of the 23^{rd} International Joint Conference on
Artificial Intelligence (IJCAI’2013), pp.
17571763, Beijing,
August 2013. [Paper]
[C.022] Y.M.
Cheung
and H. Jia, “A Unified Metric for Categorical and Numerical Attributes in Data
Clustering”, Proceedings of the 17^{th}
PacificAsia Conference on Knowledge Discovery and Data Mining
(PAKDD’2013), Vol. 2, LNAI
7819, pp.
135146, April 1417, Gold Coast,
Australia, 2013.
[C.021] X. Liu
and Y.M. Cheung, “A Multiboosted
HMM Approach to Lip Password Based Speaker Verification”, Proceedings of 2012 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP’2012), pp. 21972200, Kyoto, Japan, March
2530, 2012.
[C.020] S.J. Peng, X. Liu and Y.M. Cheung, “Subspace Based Active
Contours with a Joint Distribution Metric for Semisupervised Natural Image
Segmentation”, Proceedings of 2012 IEEE
International Conference on Acoustics, Speech and Signal Processing
(ICASSP’2012), pp. 11731176, Kyoto, Japan, March 2530, 2012.
[C.019] Y.M.
Cheung
and M. Li, “MAPMRF Based Lip Segmentation without True Segment Number”, Proceedings of 19^{th} IEEE
International Conference on Image Processing (ICIP’11), pp.
781784, September 1114, 2011.
[C.018] X. Liu
and Y.M. Cheung, "A Robust Lip Tracking Algorithm Using Localized
Color Active Contours and Deformable Models", Proceedings of 2011
International Conference on Acoustics, Speech and Signal Processing
(ICASSP'11), pp. 11971200, 2011. [Paper]
[C.017] S.J.
Peng, X. Liu and Y.M. Cheung,
“Active Contours with a Novel Distribution Metric for Complex Object
Segmentation”, Proceedings of 19^{th}
IEEE International Conference on Image Processing
(ICIP’11),
pp. 34063409, September 1114, 2011.
[C.016]
Y.M.
Cheung
and H. Jia, "A Cooperative and Penalized Competitive Learning Approach to
Gaussian Mixture Clustering", Proceedings of the 20th International
Conference on Artificial Neural Networks (ICANN'10), Lecture Notes in
Computer Science (LNCS 6354), pp. 435440, 2010. [Paper]
[C.015] M. Li
and Y.M. Cheung, "Automatic Segmentation of Color Lip Images Based
on Morphological Filter", Proceedings of the 20th International
Conference on Artificial Neural Networks (ICANN'10), Lecture Notes in
Computer Science (LNCS 6352), pp. 384387, 2010. [Paper]
[C.014] X.
Liu, Y.M. Cheung, M. Li and H.L. Liu, "A Lip Contour Extraction
Method Using Localized Active Contour Model with Automatic Parameter
Selection", Proceedings of 2010 International Conference on Pattern
Recognition (ICPR'2010), pp. 43324335, 2010. [Paper]
[C.013] Q.M.
Peng, X.G. You, L. Zhou and Y.M. Cheung, "Retinal Blood Vessels
Segmentation Using the Radial Projection and Supervised Classification", Proceedings
of 2010 International Conference on Pattern Recognition
(ICPR'2010), pp. 14891492, 2010.
[Paper]
[C.012] P.
Winoto and Y.M. Cheung and J.M. Liu, "Mechanism Design for
Clustering Aggregation by Selfish Systems", Proceedings of 7th IEEE International
Conference on Data Mining (ICDM'07), pp. 703708, 2007. [Paper]
[C.011]
Y.M.
Cheung
and H. Zeng, "A Maximum Weighted Likelihood Approach to Simultaneous Model
Selection and Feature Weighting in Gaussian Mixture", Proceedings of
2007 International Conference on Artificial Neural Networks (ICANN'07),
LNCS 4668, Part I: pp. 7887, 2007.
[Paper]
[C.010] H.T.
Wu and Y.M. Cheung, "A HighCapacity Data Hiding Method for
Polygonal Meshes", Proceedings of 8th Information Hiding (IH'06),
2006. [Paper]
[C.009] H.T.
Wu and Y.M. Cheung, "A Fragile Watermarking Scheme for 3D
Meshes", Proceedings of the 7th Workshop on Multimedia & Security
(ACM'05), pp. 117123, 2005.
[Paper]
[C.008] H.L.
Liu and Y.M. Cheung, "A Learning
Framework for Blind Source Separation Using Generalized Eigenvalues",
Proceedings of International Symposium on Neural Network, LNCS 3497, pp.
472477, Chongqin, 2005.
[Paper]
[C.007] Y.M.
Cheung,
"A Rival Penalized EM Algorithm towards Maximizing Weighted Likelihood for
Density Mixture Clustering with Automatic Model Selection", Proceedings
of the 17th International Conference on Pattern Recognition (ICPR'04), Vol.
4, pp. 633636, Cambridge, United Kingdom, 2004. [Paper]
[C.006] Y.M.
Cheung,
"A Competitive and Cooperative Learning Approach to Robust Data
Clustering", Proceedings of the IASTED International Conference on
Neural Networks and Computational Intelligence (NCI'2004), pp. 131136,
Grindelwald, Switzerland, 2004.
[Paper]
[C.005] Y.M.
Cheung
and Hailin Liu, "A New Approach to Blind Source Separation with Global
Optimal Property", Proceedings of the IASTED International Conference
of Neural Networks and Computational Intelligence (NCI'2004), pp. 137141,
Grindelwald, Switzerland, 2004.
[Paper]
[C.004]
Y.M.
Cheung
and R.B. Huang, "An Advance on DivideandConquer Based Radial Basis
Function Networks", Proceedings of Fourth International Conference on
Intelligent Data Engineering and Automated Learning (IDEAL'2003), pp.
143150, Springer Publisher, Hong Kong, March 2123, 2003. [Paper]
[C.003] L.T.
Law and Y.M. Cheung, "Color Image Segmentation Using Rival
Penalized Controlled Competitive Learning", Proceedings of 2003
International Joint Conference on Neural Networks (IJCNN'2003), Portland,
Oregon, USA, July 2024, 2003. [Paper]
[C.002]
Y.M.
Cheung,
"Rival Penalization Controlled Competitive Learning for Data Clustering
with Unknown Cluster Number", Proceedings of 9th International
Conference on Neural Information Processing (Paper ID: 1983 in CDROM
Proceeding), Singapore, November 1822, 2002. [Paper]
[C.001] Y.M. Cheung, "A New Recurrent Radial Basis Function Network", Proceedings of 9th International Conference on Neural Information Processing (Paper ID: 1538 in CDROM Proceeding) , Singapore, November 1822, 2002. [Paper]
Book Chapter:
[B.001] Z.B. Lai, Y.M. Cheung and L. Xu, "Independent Component Ordering in ICA Analysis of Financial Data", Computational Finance, Chapter 14, pp. 201212, edited by Yaser S. AbuMostafa, Blake LeBaron, Andrew W. Lo and Andreas S. Weigend, The MIT Press, 1999.
CoEdited Books:
1. Computational Intelligence and
Security, Lecture Notes in Artificial Intelligence (LNAI) (Part I: LNAI 3801,
Part II: LNAI 3802), Springer, 2005.
2. Intelligent Data
Engineering and Automated Learning, Lecture Notes in Computer Science (LNCS
2690), Springer, 2003.
List of Taught Courses:
COMP1170
Introduction to Structured Programming
COMP 2015 Data
Structures and Algorithms
COMP
2440 Database Systems II
COMP3005
Design and Analysis of Algorithms
SCI3790
Machine Learning: Theories & Applications
COMP4015 Artificial Intelligence and Machine Learning
COMP7015
Artificial Intelligence
COMP7640 Database Systems &
Administration