Bingbing Jiang 江兵兵

Bingbing Jiang 江兵兵

Associate Professor, Hangzhou Normal University

📝 Publications

Note: * indicates the corresponding author, and † indicates equal contribution.

2026

  1. Bingbing Jiang, Jie Wen, Zidong Wang, Weiguo Sheng, Zhiwen Yu, Huanhuan Chen, Weiping Ding. Scalable Semi-supervised Learning with Discriminative Label Propagation and Correction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 48(6): 6156-6173, 2026.
  2. Yadi Wang, Fan Zhang, Bingbing Jiang*. Robust multi-view clustering via quadratic matrix factorization with manifold learning. IEEE Transactions on Image Processing, 2026 (Accepted).
  3. Yan Zhong, Xingyu Wu, Xinping Zhao, Li Zhang, Xinyuan Song, Lei Shi, Bingbing Jiang. Semi-Supervised Multi-Label Feature Selection with Consistent Sparse Graph Learning. Neural Networks, 2026 (Accepted).
  4. Yadi Wang, Bingbing Jiang*. Structural Feature Selection in Common Spatial Patterns Using Adaptive Sparse Group Lasso. CAAI Transactions on Intelligence Technology, 11(2): 367-384, 2026.
  5. Yang Fang, Yujie Wang, Bingbing Jiang, Zongyi Xu, Jiaxu Leng, Yan Zhang, Weisheng Li, Xinbo Gao. M-STEP: Multi-Scale Temporal Information Enhancement and Propagation for Hierarchical Visual Transformer Tracking. IEEE Transactions on Multimedia, 2026 (Accepted).
  6. Bingbing Jiang, Zhongli Wang, Jie Yang, Guang-Kui Xu, Wei Chen, et al. Self-Enhanced Density Clustering for High Dimension and Low Sample Size Data. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2026: 508-519.
  7. Tongxue Zhou, Mingyang Li, Su Ruan, Tingjin Luo, Bingbing Jiang, et al. A reliable framework for brain tumor segmentation via multi-modal fusion and uncertainty modeling. Information Fusion, 129: 104085, 2026.
  8. Jie Yang, Cheng-You Lu, Zhongli Wang, Hsiang-Ting Chen, Guang-Kui Xu, Chenglong Zhang, Shuting Dong, Xinyan Liang, Bingbing Jiang*. Multi-View Clustering with Granularity-Aware Pseudo Supervision. AAAI Conference on Artificial Intelligence, 2026: 27538-27546.
  9. Junyi Guan, Bingbing Jiang, Weiguo Sheng, Yangyang Zhao, et al. Peak-Padding: Clustering by Padding Density Peaks With the Minimum Padding Cost. IEEE Transactions on Neural Networks and Learning Systems, 37(1): 342-356, 2026.

2025

  1. Jiale Zhou, Yan Chen, Bingbing Jiang, Peng Zhou, et al. Robust Tensor Learning with Graph Diffusion for Scalable Multi-view Graph Clustering. ACM International Conference on Multimedia, 2025: 2207-2215.
  2. Bingbing Jiang, Chenglong Zhang, Xinyan Liang, et al. Scalable Fuzzy Clustering with Collaborative Structure Learning and Preservation. IEEE Transactions on Fuzzy Systems, 33(9): 3047-3060, 2025.
  3. Jie Yang, Wei Chen, Feng Liu, Peng Zhou, Zhongli Wang, Xinyan Liang, Bingbing Jiang*. Multi-view Clustering via Multi-granularity Ensemble. International Joint Conference on Artificial Intelligence, 2025: 6794-6802.
  4. Yan Chen, Bingbing Jiang, Peng Zhou, Lei Duan, Yuhua Qian, Liang Du. Balanced Multiple Kernel Clustering with Discrete Partition Entropy Auto Regularization. ACM International Conference on Multimedia, 2025: 2197-2206.
  5. Bingbing Jiang, Chenglong Zhang, Xinyan Liang, et al. Collaborative Similarity Fusion and Consistency Recovery for Incomplete Multi-view Clustering. AAAI Conference on Artificial Intelligence, 2025: 21411-21419.
  6. Zhongli Wang, Jie Yang, Junyi Guan, Chenglong Zhang, Xinyan Liang, Bingbing Jiang, Weiguo Sheng. Enhanced Density Peak Clustering for High-dimensional Data. AAAI Conference on Artificial Intelligence, 2025: 17617-17625.
  7. Zhaolong Ling, Jiale Yu, Yiwen Zhang, Debo Cheng, Peng Zhou, Xingyu Wu, Bingbing Jiang, Kui Yu. Local Causal Discovery Without Causal Sufficiency. AAAI Conference on Artificial Intelligence, 2025: 18737-18745.
  8. Bingbing Jiang, Jun Liu, Zidong Wang, Jie Yang, Yadi Wang, Weiguo Sheng, Chenglong Zhang, Weiping Ding. Semi-supervised Multi-view Feature Selection with Adaptive Similarity Fusion and Learning. Pattern Recognition, 159: 111159, 2025.
  9. Zhaolong Ling, Jingxuan Wu, Yiwen Zhang, Peng Zhou, Bingbing Jiang, Kui Yu, Xindong Wu. Causal Feature Selection With Imbalanced Data. IEEE Transactions on Emerging Topics in Computational Intelligence, 9(2): 1610-1626, 2025.

2024

  1. Chenglong Zhang, Xinjie Zhu, Zidong Wang, Yan Zhong, Weiguo Sheng, Weiping Ding, Bingbing Jiang*. Discriminative Multi-View Fusion via Adaptive Regression. IEEE Transactions on Emerging Topics in Computational Intelligence, 8(6): 3821-3833, 2024.
  2. Bingbing Jiang, Xingyu Wu, Xiren Zhou, et al. Semi-supervised multiview feature selection with adaptive graph learning. IEEE Transactions on Neural Networks and Learning Systems, 35(3): 3615-3629, 2024.
  3. Chenglong Zhang, Yang Fang, Xinyan Liang, Han Zhang, Peng Zhou, Xingyu Wu, Jie Yang, Bingbing Jiang*, Weiguo Sheng. Efficient Multi-view Unsupervised Feature Selection with Adaptive Structure Learning and Inference. International Joint Conference on Artificial Intelligence, 2024: 5443-5452.
  4. Chenglong Zhang, Xinyan Liang, Peng Zhou, Zhaolong Lin, Yingwei Zhang, Xinyu Wu, Weiguo Sheng, Bingbing Jiang*. Scalable Multi-view Unsupervised Feature Selection with Structure Learning and Fusion. ACM International Conference on Multimedia, 2024: 5479-5488.
  5. Xingyu Wu, Yan Zhong, Zhaolong Ling, Jie Yang, Li Li, Weiguo Sheng, Bingbing Jiang*. Nonlinear learning method for local causal structures. Information Sciences, 654: 119789, 2024.
  6. Zihao Xu, Chenglong Zhang, Zhaolong Ling, Peng Zhou, Yan Zhong, Li Li, Han Zhang, Weiguo Sheng, Bingbing Jiang*. Multi-View Semi-Supervised Feature Selection with Graph Convolutional Networks. International Joint Conference on Neural Networks, 2024: 1-8.
  7. Xingyu Wu, Yan Zhong, Jibin Wu, Bingbing Jiang, Kay Chen Tan. Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation. International Joint Conference on Artificial Intelligence, 2024: 5235-5244.
  8. Rongwen Li, Haiyang Hu, Liang Du, Jiarong Chen, Bingbing Jiang, Peng Zhou. One-Stage Fair Multi-View Spectral Clustering. ACM International Conference on Multimedia, 2024: 1407-1416.
  9. Yi Liu, Jiusun Zeng, Bingbing Jiang, Weiguo Sheng, Zidong Wang, Lei Xie, Li Li. Structured collaborative sparse dictionary learning for monitoring of multimode processes. Information Sciences, 666: 120444, 2024.
  10. Yadi Wang, Mengyao Huang, Liming Zhou, Hangjun Che, Bingbing Jiang. Multi-cluster nonlinear unsupervised feature selection via joint manifold learning and generalized Lasso. Expert Systems with Applications, 255: 124502, 2024.
  11. Yang Fang, Bailian Xie, Uswah Khairuddin, Zijian Min, Bingbing Jiang, Weisheng Li. DPT-tracker: Dual pooling transformer for efficient visual tracking. CAAI Transactions on Intelligence Technology, 9: 948-959, 2024.

2023

  1. Chenglong Zhang, Bingbing Jiang*, Zidong Wang, et al. Efficient multi-view semi-supervised feature selection. Information Sciences, 649: 119675, 2023.
  2. Bingbing Jiang, Chenglong Zhang, Yan Zhong, et al. Adaptive collaborative fusion for multi-view semi-supervised classification. Information Fusion, 96: 37-50, 2023.
  3. Yangfeng Lu, Chenglong Zhang, Bingbing Jiang*. Accelerated Semi-supervised Feature Selection via Adaptive Bipartite Graph. International Conference on Artificial Intelligence and Pattern Recognition, 2023: 592-598.
  4. Xingyu Wu, Bingbing Jiang, Yan Zhong, Huanhuan Chen. Multi-target Markov boundary discovery: Theory, algorithm, and application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4): 4964-4980, 2023.
  5. Xingyu Wu, Bingbing Jiang, Tianhao Wu, et al. Practical Markov Boundary Learning without Strong Assumptions. AAAI Conference on Artificial Intelligence, 2023: 10388-10398.
  6. Xingyu Wu, Bingbing Jiang, Xiangyu Wang, et al. Feature selection in the data stream based on incremental Markov boundary learning. IEEE Transactions on Neural Networks and Learning Systems, 34(10): 6740-6754, 2023.
  7. Yang Fang, Bailian Xie, Bingbing Jiang, Xuhui Ke, Yan Li. SPPT: Siamese Pyramid Pooling Transformer for Visual Object Tracking. Human-centric Computing and Information Sciences, 13(59): 1-17, 2023.
  8. Zijie Luo, Weiguo Sheng, Bingbing Jiang, Yi Liu. Structure-Guided Graphical Lasso for Process Monitoring. CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, 2023: 1-6.

2022 and before

  1. Bingbing Jiang, Junhao Xiang, Xingyu Wu, et al. Robust multi-view learning via adaptive regression. Information Sciences, 610: 916-937, 2022.
  2. Bingbing Jiang, Wenda He, Xingyu Wu, et al. Semi-Supervised Feature Selection with Adaptive Graph Learning. ACTA Electronica Sinica, 50(7): 1643-1652, 2022.
  3. Xiren Zhou, Qiuju Chen, Bingbing Jiang, Huanhuan Chen. An Underground Pipeline Mapping Method Based on Fusion of Multisource Data. IEEE Transactions on Geoscience and Remote Sensing, 60: 4511711, 2022.
  4. Yang Fang, Bei Luo, Ting Zhao, Dong He, Bingbing Jiang, Qilie Liu. ST-SIGMA: Spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting. CAAI Transactions on Intelligence Technology, 7: 744-757, 2022.
  5. Yi Liu, Jiusun Zeng, Lei Xie, Bingbing Jiang, Dongping Zhang. Row-column overcomplete structured dictionary learning for enhanced fault detection and isolation. IEEE Transactions on Industrial Informatics, 19(5): 7032-7043, 2022.
  6. Xingyu Wu, Zhenchao Tao, Bingbing Jiang, et al. Domain knowledge-enhanced variable selection for biomedical data analysis. Information Sciences, 606: 469-488, 2022.
  7. Yadi Wang, Wenbo Zhang, Minghu Fan, Qiang Ge, Baojun Qiao, Xianyu Zuo, Bingbing Jiang. Regression with adaptive lasso and correlation based penalty. Applied Mathematical Modelling, 105: 175-196, 2022.
  8. Bingbing Jiang, Junhao Xiang, Xingyu Wu, et al. Robust adaptive-weighting multi-view classification. ACM International Conference on Information & Knowledge Management, 2021: 3117-3121.
  9. Xingyu Wu, Bingbing Jiang, Kui Yu, Huanhuan Chen. Separation and Recovery Markov Boundary Discovery and Its Application in EEG-based Emotion Recognition. Information Sciences, 571(9): 262-278, 2021.
  10. Yaqiang Yao, Yang Li, Bingbing Jiang, Huanhuan Chen. Multiple kernel k-means clustering by selecting representative kernels. IEEE Transactions on Neural Networks and Learning Systems, 32(11): 4983-4996, 2021.
  11. Yan Zhong, Xingyu Wu, Bingbing Jiang, Huanhuan Chen. Multi-label Local-to-Global Feature Selection. International Joint Conference on Neural Networks, 2021: 1-8.
  12. Yang Li, Bingbing Jiang, Huanhuan Chen, Xin Yao. Symbolic sequence classification in the fractal space. IEEE Transactions on Emerging Topics in Computational Intelligence, 5(2): 168-177, 2021.
  13. Yi Liu, Jiusun Zeng, Bingbing Jiang, Xun Lang, Lei Xie. Structured Dictionary Learning for Fault Detection and Isolation. CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, 2021: 1-6.
  14. Xingyu Wu, Bingbing Jiang, Kui Yu, Huanhuan Chen, Chunyan Miao. Multi-label causal feature selection. AAAI Conference on Artificial Intelligence, 2020: 6430-6437.
  15. Xingyu Wu, Bingbing Jiang, Kui Yu, Chunyan Miao, Huanhuan Chen. Accurate Markov Boundary Discovery for Causal Feature Selection. IEEE Transactions on Cybernetics, 50(12): 4983-4996, 2020.
  16. Shengfei Lyu, Xing Tian, Yang Li, Bingbing Jiang, Huanhuan Chen. Multiclass probabilistic classification vector machine. IEEE Transactions on Neural Networks and Learning Systems, 31(10): 3906-3919, 2020.
  17. Xingyu Wu, Bingbing Jiang, Yan Zhong, Huanhuan Chen. Tolerant Markov Boundary Discovery for Feature Selection. ACM International Conference on Information & Knowledge Management, 2020: 2261-2264.
  18. Yangyan Xu, Chenxin Wu, Bingbing Jiang, Weiguo Sheng. An Adaptive Water Wave Optimization Algorithm with Enhanced Wave Interaction. IEEE Congress on Evolutionary Computation, 2020: 1-8.
  19. Bingbing Jiang, Chang Li, Maarten De Rijke, Huanhuan Chen, Xin Yao. Probabilistic feature selection and classification vector machine. ACM Transactions on Knowledge Discovery from Data, 13(2): 1-27, 2019.
  20. Bingbing Jiang, Xingyu Wu, Kui Yu, Huanhuan Chen. Joint semi-supervised feature selection and classification through Bayesian approach. AAAI Conference on Artificial Intelligence, 2019: 3983-3990.
  21. Huanhuan Chen, Bingbing Jiang, Xin Yao. Semi-supervised negative correlation learning. IEEE Transactions on Neural Networks and Learning Systems, 29(11): 5366-5379, 2018.
  22. Bingbing Jiang, Zhengyu Li, Huanhuan Chen, et al. Latent Topic Text Representation Learning on Statistical Manifolds. IEEE Transactions on Neural Networks and Learning Systems, 29(11): 5643-5654, 2018.
  23. Bingfei Chen, Bingbing Jiang*, Xiren Zhou, Huanhuan Chen. Manifold learning based on sparse Bayesian approach. ACTA Electronica Sinica, 46(1): 98-103, 2018.
  24. Wei Wang†, Bingbing Jiang†, Shandong Ye, Liting Qian. Risk factor analysis of bone mineral density based on feature selection in type 2 diabetes. IEEE International Conference on Big Knowledge, 2018: 221-226.
  25. Bingbing Jiang, Huanhuan Chen, Bo Yuan, Xin Yao. Scalable graph-based semi-supervised learning through sparse Bayesian model. IEEE Transactions on Knowledge and Data Engineering, 29(12): 2758-2771, 2017.