Jun Huang (黄俊)

安徽工业大学 , Assistant Professor Research Interests:机器学习与数据挖掘

I received the M.S. degree in computer science from Anhui University of Technology, Ma'anshan, China, in 2011, and the Ph.D. degree in computer science from School of Computer and Control Engineering, University of Chinese Academy of Sciences (UCAS), Beijing, China, in 2017, under the supervision of Prof. Qingming Huang. Now, I am a post-doctoral researcher at The University of Tokyo under the supervision of Prof. Kenji Yamanishi.  My research interest is generally in machine learning and data mining, and particularly in multi-label learning and multi-view learning.


Email: huangjun_cs@163.com

  • Post-doctoral Researcher (Oct 2019 - present), Graduate School of Information Science and Technology, The University of Tokyo, Japan

  • Assistant Professor (July 2017 - present), School of Computer Science and Technology, Anhui Univesity of Technology, China

  • Assistant Experimentalist (July 2011 - August 2013), School of Computer Science and Technology,  Anhui Univesity of Technology, China

  • Ph.D. (Sep 2013 - July 2017): School of Computer Science and Technology, University of Chinese Academy of Sciences, China

  • M.S (Sep 2008 - July 2011): School of Computer Science and Technology, Anhui Univesity of Technology, China


  1. Multi-View Multi-Label Learning With View-Label-Specific Features
    J. Huang, X. Qu and G. Li, F. Qin, X. Zheng and Q. Huang. IEEE Access: 2019 ,7 ,100979-100992
  2. Beyond Global Fusion: A Group-Aware Fusion Approach for Multi-View Image Clustering
    Zhe Xue, Guorong Li, Shuhui Wang, Jun Huang, Weigang Zhang and Qingming Huang. Information Sciences: 2019 ,493 ,176-191
  3. Improving Multi-Label Classification with Missing Labels by Learning Label-Specific Features
    Jun Huang, Feng Qin, Xiao Zheng, Zekai Cheng, Zhixiang Yuan, Weigang Zhang, and Qingming Huang. Information Sciences: 2019 ,492 ,124-146
  4. Learning Label-Specific Features for Multi-Label Classification with Missing Labels
    J. Huang, F. Qin, X. Zheng, Z. Cheng, Z. Yuan, and W. Zhang. IEEE BigMM: 2018 ,1-5
  5. Joint Feature Selection and Classification for Multilabel Learning
    J. Huang, G. Li , Q. Huang and X. Wu. IEEE Transactions on Cybernetics: 2018 ,48(3) ,876-889
  6. Multi-label classification by exploiting local positive and negative pairwise label correlation
    J. Huang, G. Li , S. Wang, Z. Xue and Q. Huang. Neurocomputing: 2017
  7. Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification
    J. Huang, G. Li, Q. Huang and X. Wu. TKDE: 2016
  8. Beyond Appearance Model: Learning Appearance Variations for Object Tracking
    G. Li, B. Ma, J. Huang, Q. Huang and W. Zhang. Neurocomputing: 2016
  9. Learning label Specific Features for Multi-Label Classification
    J. Huang, G. Li, Q. Huang and X. Wu. ICDM: 2015 Download
  10. Group sensitive Classifier Chains for multi-label classification
    J, Huang, G. Li, S. Wang, W. Zhang and Q. Huang. Multimedia and Expo (ICME), 2015 IEEE International Conference on: 2015(June) ,1-6
  11. Categorizing Social Multimedia by Neighborhood Decision Using Local Pairwise Label Correlation
    J. Huang, G. Li , S. Wang and Q. Huang. Data Mining Workshop (ICDMW), 2014 IEEE International Conference on: 2014 ,913-920
  • J. Huang, X. Qu, G. Li, F. Qin, X. Zheng, Z. Cheng,  and Q. Huang, Multi-View Multi-Label Learning with View-Label-Specific Features, IEEE Access: 2019 (code.zip). generateCVSet.m

  • J. Huang, F. Qin, X. Zheng, Z. Cheng, Z. Yuan, W. Zhang, and Q. Huang, Improving multi-label classification with missing label by learning label-specific features, Information Sciences: 2019. (code.rar

  • J. Huang, G. Li, S. Wang, Z. Xue and Q. Huang, Multi-label classification by exploiting local positive and negative pairwise label correlation, Neurocomputing: 2017(code)

  • J. Huang, G. Li, Q. Huang and X. Wu. Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification, TKDE: 2016 (code)

  • J. Huang, G. Li, Q. Huang and X. Wu. Learning label Specific Features for Multi-Label Classification, ICDM: 2015 (code)

Experiment datasets in .mat format


The above datasets are collected from the following websites:

We strongly suggest you to cite their corresponding papers of these datasets if you evaluate them in your research paper(s).

  • Computer Science and Technology: An Overview (for undergraduate students), Fall, 2017, 2018

  • Data Mining (for graduate students), Spring, 2018

  • Data Mining (for international graduate students), Fall, 2018

  • Data Structure (for undergraduate students), Fall, 2018

  • Merit Student of University of Chinese Academy of Sciences, 2016

  • President Award of Chinese Academy of Sciences, 2017

  • Excellent Ph.D. Dissertation of Chinese Academy of Sciences, 2018

PC Member:

  • ACM International Conference on Multimedia (ACMMM) 2019

  • ACM Multimedia Asia 2019

  • CCF China MM 2019


External Reviewer:

  • IEEE Transaction on Knowledge and Data Engineering (2019-)

  • International  Journal of Machine Learning and Cybernetics (2019-)

  • IEEE Transactions on Cybernetics (2018-)

  • ACM Transactions on Knowledge Discovery from Data (2018-)

  • Neural Networks (2018-)

  • IEEE Access (2018-)

  • Multimedia Tools and Applications (2017-)

  • DASFAA'18, BigMM'18

  • ICDM'16, NCMT'16

















Updated on:2019-12-06 14:42      Total Visits:3895

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