mail address:   IDEA lab, BRICThe University of North Carolina at Chapel Hill,
                         3114P, CB #7513, 130 Mason Farm Road, Chapel Hill, NC 27599, USA
 email:              xiaofeng AT med DOT unc DOT edu
Xiaofeng Zhu received his BSc degree (in Mathematics) and ME degree (in Computer Science) from  Guangxi Normal University (GXNU), China,  then obtained his MSc degree (by research in computer science) and PhD degree (in computer science), respectively,  from National University of Singapore (NUS), Singapore and The University of Queensland (UQ), Australia. He was a research assistant/exchange student/visiting research scholar at Chinese Academy of Sciences (CAS), China, University of Technology,Sydney (UTS), Australia,  The Hong Kong Polytechnic University, HongKong (HKPolyU), China, and UNC-CH, respectively. He is currently a post-doctoral research associate at the University of North Carolina at Chapel Hill (UNC-CH), USA.  
Research interests:
Feature selection, subspace learning, sparse coding, multi-task learning, missing value imputation, cost-sensitive learning, hashing, ...
Recent Papers (DBLP  Full List  Google Citation):  
  Journal papers    
  •    X. Zhu, X. Li, S. Zhang, C. Ju, X. Wu, Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection, IEEE Transactions on Neural Networks and Learning Systems, accepted, 2016  (PDF) (code)
  •    X. Zhu, H.I. Suk, S.W. Lee, and D. Shen, Subspace Regularized Sparse Multi-Task Learning for Multi-Class Neurodegenerative Disease Identification”, IEEE Trans. on BME, 63(3):    607-618, 2016.
    (PDF) (code)
  •    X. Zhu, X. Li, and S. Zhang, Block-Row Sparse Multiview Multilabel Learning for Image Classification, IEEE Transactions on Cybernetics, 46(2): 450-461, 2016. (PDF) (code)
  •    X. Zhu, H.I. Suk, L. Wang, S.-W. Lee, D. Shen,A Novel Relational Regularization Feature Selection Method for Joint Regression and Classification in AD Diagnosis, Medical Image Analysis, 2015. (PDF) (code)
  •    X. Zhu, H.I. Suk, S.W. Lee, and D. Shen, Canonical Feature Selection for Joint Regression and Multi-class Identification in Alzheimer's Disease Diagnosis, Brain Imaging and Behavior, 2015. (PDF) (code)
  •    X. Zhu, Q. Xie, Y. Zhu, Z. Zhong, and S. Zhang:Multi-View Kernel Sparse Regression for Visual Classification, Neurocomputing, 169: 43-49, 2015. (PDF) (code)
  •    X. Zhu, L. Zhang and Z. Huang, A Sparse Embedding and Least Variance Encoding Approach to Hashing, IEEE Transactions on Image Processing, 2014.  (PDF) (code)
  •    X. Zhu, H.I. Suk, and D. Shen: A Novel Matrix-Similarity Based Loss Function for Joint Regression and Classification in AD Diagnosis. NeuroImage, 2014. (PDF) (code)
  •    X. Zhu, Z. Huang, H. Cheng, J. Cui and H. T. Shen. "Sparse Hashing for Fast Multimedia Search". ACM Transactions on Information Systems, 31(2),2013. (PDF) (code)
  •    X. Zhu, Z. Huang, J. Cui, H. T. Shen. "Video-to-Shot Tag Propagation by Graph Sparse Group Lasso". IEEE Transactions on Multimedia, 15(3): 633 - 646, 2013.(PDF) (code)
  •    X. Zhu, Z. Huang, Y. Yang, H. T. Shen, C. Xu, J. Luo: Self-taught dimensionality reduction on the high-dimensional small-sized data. Pattern Recognition 46(1): 215-229 (2013) (PDF) (code)
  •    X. Zhu,  Z. Huang, H. T. Shen, J. Cheng and C. Xu. " Dimensionality reduction by mixed kernel canonical correlation analysis''. Pattern Recognition, 45(8):3003–3016, 2012 (PDF) (code)
  •    X. Zhu, S. Zhang, Z. Jin, Z. Zhang, Z. Xu (2011): Missing Value Estimation for Mixed-Attribute Datasets, IEEE Transactions on Knowledge and Data Engineering (TKDE), Jan. 2011 , vol: 23(1), 110 -121. (PDF
 Conference papers
  •   X. Zhu, H.I. Suk, H. Huang, D. Shen,“Structured Spare Low-Rank Regression Model for Brain-Wide and Genome-Wide Associations”, MICCAI 2016. (PDF) (code)
  •   J. Peng, L. An, X. Zhu, Y. Jin, D. Shen,“Structured Sparse  Kernel Learning for Imaging Genetics based AD Diagnosis”, MICCAI 2016.
  •   Y. Zhu, X. Zhu, H. Zhang, W. Gao, D. Shen, G. Wu,“Reveal Consistent Spatial-Temporal Patterns from Dynamic Functional Connectivity for Autism Spectrum Disorder Identification”, MICCAI 2016. 
  •   Y. Zhu, X. Zhu, M. Kim, Di. Shen, G. Wu,“Early Diagnosis of Alzheimer’s Disease by Joint Feature Selection and Classification on Temporally Structured Support Vector Machine”, MICCAI 2016. 
  •   Z. Wang, X. Zhu, E. Adeli, Y. Zhu, C. Zu, F. Nie, D. Shen, G. Wu,“Progressive Graph-Based Transductive Learning for Multi-Modal Classification of Alzheimer’s Disease”, MICCAI 2016.
  •   L. Gao, J. Song, J. Shao, X. Zhu, and H.T. Shen, Zero-shot Image Categorization by Image Correlation Exploration, ICMR 2015.
  •   G. Wu, P. Zhang, Q. Wang, X. Zhu, D. Shen,Image Super-Resolution by Supervised Adaption of Patchwise Self-Similarity from High-Resolution Image, MICCAI Workshop on Patch-MI, 2015.
  •   G. Sanroma, O. Benkarim, G. Piella, G. Wu, X. Zhu, D.Shen, M. Ballester, Discriminative Dimensionality Reduction for Patch-based Label Fusion, ICML Workshop on MLMI, 2015.
  •   X. Zhu H.I. Suk, K.H. Thung, G. Wu, D. ShenMulti-View Classification for Identification of Alzheimer’s Disease", MLMI 2015.
  •   X. Zhu, H.I. Suk, and D. Shen: A Novel Multi-Relation Regularization Method for Regression and Classification in AD Diagnosis. In Proceedings of MICCAI 2014.
  •   X. Zhu, H.I. Suk, and D. Shen: Multi-Modality Canonical Feature Selection for Alzheimer's Disease Diagnosis. In Proceedings of MICCAI 2014.
  •   X. Zhu, H.I. Suk, and D. Shen: Sparse Discriminative Feature Selection for Multi-Class Alzheimer's Disease Classification. In Proceedings of MLMI 2014.
  •   X. Zhu, H.I. Suk, and D. Shen: Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer's Disease Diagnosis. In Proceedings of CVPR 2014.
  •   H. Cai , Z. Huang, X. Zhu, Q. Zhang, X. Li, Multi-Output Regression with Tag Correlation Analysis for Effective Image Tagging. In Proceedings of DASFAA 2014.
  •   X. Zhu, Z. Huang, H. T. Shen, and X. Zhao,Linear Cross-Modal Hashing for Effective Multimedia Search. In Proceedings of ACM MM, 143-152, 2013. (PDF) (code)
  •   X. Zhao, X. Li, C. Pang, X. Zhu, and M. Sheng,Online Human Gesture Recognition from Motion Data Streams. In Proceedings of ACM MM, 23-32, 2013.
  •   X. Zhu, X. Wu, W. Ding and S. Zhang (2013), Feature selection by joint sparse coding with a graph regularizer. In Proceedings of SIAM International Conference on Data Mining (SDM2013),
  •   X. Zhu, Z. Huang and X. Wu (2013), Multi-view Visual Classification via a Mixed-norm Regularizer. In Proceedings of PAKDD 2013, Part I, LNAI 7818, pp. 520–531, 2013.
  •   X. Zhu, J.Zhang, S. Zhang, Mixed-Norm Regression for Visual Classification.  In Proceedings of ADMA2013.
  •   J. Zhang, X. Zhu, X. Li, S. Zhang, Mining Popular Items for Recommender Systems. In Proceedings of ADMA2013.
  •   X. Zhu, Z. Huang and H. T. Shen. "Video-to-Shot Tag Allocation by Weighted Sparse Group Lasso". In Proceedings of 19th ACM International Conference on Multimedia, pages 1501-1504, 2011.
 Research Activities:

    Editorial Role

  • [Editorial Board Member]  International Journal of Data Mining and Bioinformatics
  • [Managing Guest Editor]    Neurocomputing (LMI-SI and LMD-SI)
                                                      Multimedia Systems (KDMD-SI)

                                                      Multimedia Tools and Applications (MDUI-SI)
  • [Publications Chair]            FAW 2015 (The 9th International Frontiers of Algorithmics Workshop)

    Invited Reviewer of Journals:

  • TKDE, TIP, TNNLS, TMM, PR, TBME, IS, KIS, CIVU, MTA, KBS, MSSJ, Neurocomputing, Signal Processing, Neural Computing and Applications, International Journal of Machine Learning and Cybernetics, SCIENCE CHINA (Information Sciences), Journal of Applied Mathematics, PLOS ONE, Computer Methods and Programs in Biomedicine, IEEE Signal Processing Letters, information science, IEEE Transactions on Cloud Computing,...
    Program Committee Member of Conferences
  • 2017:   MMM, AAAI
  • 2016:   PRICAI, FAW, SKG, ADMA, PatchMI, MLMI
  • 2015:   ACM MM, ICDM, ECML/PKDD, CCF Big Data, AITP, FAW


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