Huang Jun

Machine Learning and Data Mining

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Welcome

I am an associate professor at Anhui University of Technology, Ma’anshan, China. My research interest is generally in machine learning and data mining, and particularly in multi-label learning and multi-view learning.

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 Science and Technology, University of Chinese Academy of Sciences (UCAS), Beijing, China, in 2017, under the supervision of Prof. Qingming Huang. From Oct 2019 to Oct 2020, I was a post-doctoral researcher at The University of Tokyo under the supervision of Prof. Kenji Yamanishi.

Education & Experience

  • Associate Professor (Dec 2020 - present), School of Computer Science and Technology, Anhui Univesity of Technology, China
  • Post-doctoral Researcher (Oct 2019 - Oct 2020), Graduate School of Information Science and Technology, The University of Tokyo, Japan
  • Assistant Professor (July 2017 - Dec 2020), 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

Publication

  1. Jun Huang, Yang Yang, Hang Yu, Jianguo Li, Xiao Zheng, Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System, the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE2023), 2023. (CCF A) (code)
  2. Xiwen Qu, Hao Che, Jun Huang, Linchuan Xu, and Xiao Zheng, Multi-layered semantic representation network for multi-label image classification, International Journal of Machine Learning and Cybernetics, 14, pages3427–3435, 2023. (code,PDF,Paperswithcode)
  3. Jun Huang, Dian Wang, Xudong Hong, Xiwen Qu, and Wei Xue, Cross-modality semantic guidance for multi-label image classification, Intelligent Data Analysis, 2023. (CCF C)
  4. L Duan, W Xue, J Huang, X Zheng, Joint Sample Position Based Noise Filtering and Mean Shift Clustering for Imbalanced Classification Learning, Tsinghua Science and Technology, 29 (1), 216-231, 2023.
  5. Y Ge, W Xue, Y Xu, J Huang, X Gu, Magnetic Resonance Image Denoising Based on Laplacian Prior Sparsity Constraint and Nonconvex Second-Order TV Penalty, Image Analysis & Stereology, 42 (2), 119-132, 2023.
  6. Qianqian Cheng, Jun Huang, Huiyi Zhang, Sibao Chen, and Xiao Zheng, Improving multi-label learning by modeling local label and feature correlations, Intelligent Data Analysis, 27 (2), 379-398, 2023. (CCF C)
  7. Yong Peng, Honggang Liu, Junhua Li, Jun Huang, Baoliang Lu and Wanzeng Kong, Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features Exploration, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 759-768, 2023.
  8. Zipei Yan, Linchuan Xu, Atsushi Suzuki, Jing Wang, Jiannong Cao, and Jun Huang, RGB Color Model Aware Computational Color Naming and Its Application to Data Augmentation, IEEE International Conference on Big Data (Big Data), 2022. (CCF C)
  9. Minghong Ye, Xiwen Qu, Jun Huang, and Xuangou Wu, In-air Handwriting System Based on Improved YOLOv5 algorithm and Monocular Camera, IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI), 949-954, 2022. (CCF C)
  10. M Hu, X Qu, J Huang, and X Wu, An End-to-End Classifier Based on CNN for In-Air Handwritten-Chinese-Character Recognition, Applied Sciences 12(14), 6862, 2022.
  11. Jun Huang, Yu Yan, Xiao Zheng, Xiwen Qu, and Xudong Hong, Discovering Unknown Labels for Multi-Label Image Classification, ICDM Workshop, 2022.
  12. Xiuyan Hao, Jun Huang, Feng Qin, and Xiao Zheng, Multi-label learning with missing features and labels and its application to text categorization, Intelligent Systems with Applications, 2022.
  13. Jun Huang,Qian Xu, Xiwen Qu, Yaojing Lin, and Xiao Zheng, Improving Multi-Label Learning by Correlation Embedding, Applied Sciences, 2022.
  14. Liang Zhang, Kun Qian, Jun Huang, Mao Liu, and Yasushi Shibuta, Molecular dynamics simulation and machine learning of mechanical response in non-equiatomic FeCrNiCoMn high-entropy alloy, Journal of Materials Research and Technology, 13:2043-2054,2021. (PDF)
  15. Linchuan Xu, Jun Huang, Atsushi Nitanda, Ryo Asaoka, Kenji Yamanishi, A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification, arXiv, 2020. (PDF)
  16. Jun Huang, Linchuan Xu, Kun Qian, Jing Wang, and Kenji Yamanishi, Multi-Label Learning with Missing and Completely Unobserved Labels, Data Mining and Knowledge Discovery, 35:1061–1086, 2021. (CCF B)[PDF]
  17. Lei Feng, Jun Huang, Senlin Shu, Bo An, Regularized Matrix Factorization for Multilabel Learning With Missing Labels, IEEE Transactions on Cybernetics, 2020. (CCF B)
  18. Jun Huang, Linchuan Xu, Jing Wang, Lei Feng, Kenji Yamanishi, Discovering Latent Class Labels for Multi-Label Learning, The 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence(IJCAI-PRICAI), 2020. (CCF A)
  19. Jun Huang, Guorong Li, Qingming Huang and Xindong Wu, Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification, IEEE Transactions on Knowledge and Data Engineering, 28(12):3309-3323, 2016. (CCF A) (code)
  20. Jun Huang, Guorong Li , Qingming Huang and Xindong Wu, Joint Feature Selection and Classification for Multilabel Learning, IEEE Transactions on Cybernetics, 48(3):876-889, 2018. (CCF B)
  21. Jun Huang, Feng Qin, Xiao Zheng, Zekai Cheng, Zhixiang Yuan, Weigang Zhang, and Qingming Huang, Improving Multi-Label Classification with Missing Labels by Learning Label-Specific Features, Information Sciences, 492:124-146, 2019. (CCF B) (code)
  22. Jun Huang, Guorong Li, Qingming Huang and Xindong Wu, Learning label Specific Features for Multi-Label Classification, IEEE International Conference on Data Mining (ICDM), 181–190, 2015. (CCF B) (code)
  23. Jun Huang, Guorong Li, Shuhui Wang, Weigang Zhang and Qingming Huang, Group sensitive Classifier Chains for multi-label classification, IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2015. (CCF B)
  24. Jun Huang, Guorong Li, Shuhui Wang, Zhe Xue and Qingming Huang, Multi-label classification by exploiting local positive and negative pairwise label correlation, Neurocomputing, 257:164-174, 2017. (CCF C) (code)
  25. Jun Huang, Xiwen Qu and Guorong Li, Feng Qin, Xiao Zheng and Qingming Huang, Multi-View Multi-Label Learning With View-Label-Specific Features, IEEE Access, 7:100979-100992, 2019.(code, generateCVSet.m)
  26. Jun Huang, Haowei Rui, Guorong Li, Xiwen Qu, Taotao Tao, Xiao Zheng, Multi-Label Learning With Hidden Labels, IEEE Access, 8:29667-29676, 2020.
  27. Jun Huang, Pingzhao Zhang, Huiyi Zhang, Guorong Li, Haowei Rui, Multi-Label Learning via Feature and Label Space Dimension Reduction, IEEE Access, 8:20289-20303, 2020.
  28. Li Huang, Xiao Zheng, Shuai Ding, Zhi Liu, Jun Huang, Enhancing the Performance of Cuckoo Search Algorithm with Multi-Learning Strategies, IEICE Transactions on Information and Systems, 102(10):1916-1924, 2019.
  29. Zhe Xue, Guorong Li, Shuhui Wang, Jun Huang, Weigang Zhang and Qingming Huang, Beyond Global Fusion: A Group-Aware Fusion Approach for Multi-View Image Clustering, Information Sciences, 493:176-191, 2019. (CCF B)
  30. Jun Huang, Feng Qin, Xiao Zheng, Zekai Cheng, Zhixiang Yuan, and Weigang Zhang, Learning Label-Specific Features for Multi-Label Classification with Missing Labels, IEEE BigMM, 1-5, 2018.
  31. Guorong Li, Bingpeng Ma, Jun Huang, Qingming Huang and Weigang Zhang, Beyond Appearance Model: Learning Appearance Variations for Object Tracking, Neurocomputing, 2016. (CCF C)
  32. Jun Huang, Guorong Li, Shuhui Wang and Qingming Huang, Categorizing Social Multimedia by Neighborhood Decision Using Local Pairwise Label Correlation, IEEE International Conference on Data Mining Workshop (ICDMW), 913-920, 2014.

Multi-Label Data

Teaching

  • Freshman Seminar for AI Students, Spring, 2021-
  • Data Structure (for undergraduate students), Fall, 2018-
  • Data Mining (for graduate students), Spring, 2018-
  • Data Mining (for international graduate students), Fall, 2018
  • Computer Science and Technology: An Overview (for undergraduate students), Fall, 2017, 2018

Awards

  • The Rising Star Award of ACM China Councial Hefei Chapter, 2021
  • The third prize of Science and Technology Progress Award of Anhui Province, 2020
  • Excellent Ph.D. Dissertation of Chinese Academy of Sciences, 2018
  • President Award of Chinese Academy of Sciences, 2017
  • Merit Student of University of Chinese Academy of Sciences, 2016

Academic Services

  • Conference Reviewer
    • PC Member:
      • International Joint Conference on Artificial Intelligence (IJCAI) 2021-2023
      • ACM International Conference on Multimedia (ACMMM) 2019-2023
      • ACM Multimedia Asia 2019, 2021
      • ECML PKDD 2020
      • CCF China MM 2019
      • IEEE BigMM 2018
    • External Reviewr:
      • DASFAA’18, FSDM’18
      • ICDM’16, NCMT’16
  • Journal Reviewer
    • IEEE Transactions on Pattern Analysis and Machine Intelligence (2020-)
    • Artificial Intelligence Review (2020-)
    • Future Generation Computer Science (2020-)
    • Neurocomputing (2020-)
    • IEEE Transactions on Industrial Informatics (2020-)
    • 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-)

Master Student

  • 2023: Qinhao Tian, Liuwei Shi, Yaqian Wu, Jichao Ye, Rui Yao
  • 2022: Jiao Li, Sunbin Wang, Yuxuan Yang, Jun Wang
  • 2021: Yang Yang, Haodong Fan, Dian Wang, Yuanyuan Wang,Le He
  • 2020: Yu Yan, Mengqi Liu
  • 2019: Qian Xu, Jiahong Tian
  • 2018: Haowei Rui, Rui Shen (Part time)
  • Co-supervised
    • 2020: Yaqing Wang, Jiaxiang Guo, Chen Lin, Xiaolong Dong
    • 2019: Xiuyan Hao, Qianqian Chen
    • 2018: Jin Zhang, Pingzhao Zhang, Mengying Qin