Shudong Huang

Shudong Huang

Associate Professor of Computer Science, Sichuan University

Selected Publications

*indicating the corresponding author
#indicating the co-first author

Manuscript/Preprints

  1. Shudong Huang#, Wentao Feng#, Chenwei Tang, Zhenan He, Caiyang Yu, and Jiancheng Lv. Partial Differential Equations Meet Deep Neural Networks: A Survey. [arXiv] [report].
  2. Shudong Huang, Hecheng Cai, Ivor W. Tsang, Zenglin Xu, and Jiancheng Lv. Topological Manifold Learning for Multi-view Clustering.

2024

  1. Shudong Huang, Hecheng Cai, Hao Dai, Wentao Feng, and Jiancheng Lv. Adaptive Instance-wise Multi-view Clustering. In: Proceedings of the 32nd ACM International Conference on Multimedia (ACM MM’24), Melbourne, Australia, 2024, to appear. (CCF-A) [Link] [Source Code]
  2. Yuze Tan, Yixi Liu, Hongjie Wu, Shudong Huang*, Zenglin Xu, Ivor Tsang, and Jiancheng Lv. Euclidean Distance Is Not Your Swiss Army Knife. IEEE Transactions on Knowledge and Data Engineering, 2024, in press. (CCF-A) [Link] [Source Code] [IF2024: 8.9]
  3. Hao Dai, Yang Liu, Peng Su, Hecheng Cai, Shudong Huang*, and Jiancheng Lv. Multi-View Clustering by Inter-cluster Connectivity-Guided Reward. In: Proceedings of the 41st International Conference on Machine Learning (ICML’24), Vienna, Austria, 2024, pp: 9846-9855. (CCF-A) [Link] [Source Code]
  4. Peng Su, Yixi Liu, Shujian Li, Shudong Huang*, and Jiancheng Lv. Robust Contrastive Multi-view Kernel Clustering. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI’24), Jeju, South Korea, 2024, pp: 4938-4945. (CCF-A) [Link] [Source Code]
  5. Hecheng Cai, Yang Liu, Shudong Huang*, and Jiancheng Lv. With a Little Help from Language: Semantic Enhanced Visual Prototype Framework for Few-Shot Learning. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI’24), Jeju, South Korea, 2024, pp: 3751-3759. (CCF-A) [Link] [Source Code]
  6. Song Wu, Yan Zheng, Yazhou Ren, Jing He, Xiaorong Pu, Shudong Huang, Zhifeng Hao, and Lifang He. Self-Weighted Contrastive Fusion for Deep Multi-View Clustering. IEEE Transactions on Multimedia, 2024, in press. [Link] [Source Code] [IF2024: 8.4]
  7. Yuze Tan, Hecheng Cai, Shudong Huang*, Shuping Wei, Fan Yang, and Jiancheng Lv. An Effective Augmented Lagrangian Method for Fine-grained Multi-view Optimization. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI’24), Vancouver, Canada, 2024, pp: 15258-15266. (CCF-A) [Link] [Source Code]

2023

  1. Kun Zhang, Zhichu Xia, Shudong Huang, Gui-Quan Sun, Jiancheng Lv, Marco Ajelli, Keisuke Ejima, and Quan-Hui Liu. Evaluating the Impact of Test-trace-isolate for COVID-19 Management and Alternative Strategies. PLOS Computational Biology, 2023, in press. [Link] [Source Code] [IF2023: 4.3]
  2. Mingjia Shi, Yuhao Zhou, Kai Wang, Huaizheng Zhang, Shudong Huang, Qing Ye, and Jiangcheng Lv. PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. In: Advances in Neural Information Processing Systems 36 (NeurIPS’23), New Orleans, LA, 2023. (CCF-A) [Link] [Source Code]
  3. Yixi Liu, Yuze Tan, Hongjie Wu, Shudong Huang*, Yazhou Ren, and Jiancheng Lv. Preserving Local and Global Information: An Effective Metric-based Subspace Clustering. In: Proceedings of the 31st ACM International Conference on Multimedia (ACM MM’23), Ottawa, Canada, 2023, pp: 3619–3627. (CCF-A) [Link] [Source Code]
  4. Tongjie Pan, Yalan Ye, Hecheng Cai, Shudong Huang, Yang Yang, and Guoqing Wang. Multimodal Physiological Signals Fusion for Online Emotion Recognition. In: Proceedings of the 31st ACM International Conference on Multimedia (ACM MM’23), Ottawa, Canada, 2023, pp: 5879–5888. (CCF-A) [Link] [Source Code]
  5. Shudong Huang, Yixi Liu, Hecheng Cai, Yuze Tan, Chenwei Tang, and Jiancheng Lv. Smooth Representation Learning from Multi-view Data. Information Fusion, 2023, in press. [Link] [Source Code] [IF2023: 18.6]
  6. Hecheng Cai, Yuze Tan, Shudong Huang*, and Jiancheng Lv. Lifelong Multi-view Spectral Clustering. In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI’23), Macao, China, 2023, pp: 3488-3496. (CCF-A) [Link] [Source Code]
  7. Yuze Tan, Yixi Liu, Shudong Huang*, Wentao Feng, and Jiancheng Lv. Sample-level Multi-view Graph Clustering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Conference (CVPR’23), Vancouver, Canada, 2023, pp: 23966-23975. (CCF-A) [Link] [Source Code]
  8. Yuze Tan, Yixi Liu, Hongjie Wu, Jiancheng Lv, and Shudong Huang*. Metric Multi-view Graph Clustering. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI’23), Washington, DC, 2023, pp: 9962-9970. (CCF-A) [Link] [Source Code]
  9. Zongmo Huang, Yazhou Ren, Xiaorong Pu, Shudong Huang, Zenglin Xu, and Lifang He. Self-Supervised Graph Attention Networks for Deep Weighted Multi-View Clustering. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI’23), Washington, DC, 2023, pp: 7936-7943. (CCF-A) [Link] [Source Code]
  10. Hongjie Wu, Shudong Huang*, Chenwei Tang, Yancheng Zhang, and Jiancheng Lv. Pure Graph-guided Multi-view Subspace Clustering. Pattern Recognition, 2023, in press. [Link] [Source Code] [IF2023: 8.0]
  11. Xinggu Liu, Zhiming Long, Zongyuan Li, Shudong Huang, and Zhuqing Wang. An Improved Adaptive Periodical Segment Matrix Algorithm for ECG Denoising based on Singular Value Decomposition. Technology and Health Care, 2023, 31(1): 269-281. [Link] [Source Code] [IF2023: 1.6]

2022

  1. Shudong Huang#, Hongjie Wu#, Yazhou Ren, Ivor Tsang, Zenglin Xu, Wentao Feng, and Jiancheng Lv. Multi-view Subspace Clustering on Topological Manifold. In: Advances in Neural Information Processing Systems 35 (NeurIPS’22), New Orleans, LA, 2022, pp: 25883-25894. (CCF-A, Spotlight) [Link] [Source Code]
  2. Shudong Huang, Ivor W. Tsang, Zenglin Xu, and Jiancheng Lv. CGDD: Multi-view Graph Clustering via Cross-graph Diversity Detection. IEEE Transactions on Neural Networks and Learning Systems, 2022, 35(3): 4206-4219. [Link] [Source Code] [IF2022: 14.255]
  3. Shudong Huang, Yixi Liu, Ivor W. Tsang, Zenglin Xu, and Jiancheng Lv. Multi-view Subspace Clustering by Joint Measuring of Consistency and Diversity. IEEE Transactions on Knowledge and Data Engineering, 2022, 35(8): 8270-8281. (CCF-A) [Link] [Source Code] [IF2022: 9.235]
  4. Shudong Huang, Yixi Liu, Yazhou Ren, Ivor W. Tsang, Zenglin Xu, and Jiancheng Lv. Learning Smooth Representation for Multi-view Subspace Clustering. In: Proceedings of the 30th ACM International Conference on Multimedia (ACM MM’22), Lisbon, Portugal, 2022, pp: 3421–3429. (CCF-A) [Link] [Source Code]
  5. Jianjian Shao, Zhenqian Wu, Yuanyan Luo, Shudong Huang, Xiaorong Pu, and Yazhou Ren. Self-Paced Label Distribution Learning for In-The-Wild Facial Expression Recognition. In: Proceedings of the 30th ACM International Conference on Multimedia (ACM MM’22), Lisbon, Portugal, 2022, pp: 161–169. (CCF-A) [Link] [Source Code]
  6. Shudong Huang, Ivor W. Tsang, Zenglin Xu, and Jiancheng Lv. Latent Representation Guided Multi-view Clustering. IEEE Transactions on Knowledge and Data Engineering, 2022, 35(7): 7082-7087. (CCF-A) [Link] [Source Code] [IF2022: 9.235]
  7. Shudong Huang, Wei Shi, Zenglin Xu, Ivor W. Tsang, and Jiancheng Lv. Efficient federated multi-view learning. Pattern Recognition, 2022, in press. [Link] [Source Code] [IF2022: 8.518]
  8. Shudong Huang, Ivor W. Tsang, Zenglin Xu, Jiancheng Lv, and Quanhui Liu. Multi-view Clustering on Topological Manifold. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI’22), Virtual, 2022, pp: 6944-6951. (CCF-A) [Link] [Source Code]
  9. Shudong Huang, Ivor W. Tsang, Zenglin Xu, and Jiancheng Lv. Multiple Partitions Alignment via Spectral Rotation. Machine Learning, 2022, 111: 1049-1072. [Link] [Source Code] [IF2022: 5.414]
  10. Peng Zhao, Hongjie Wu, and Shudong Huang*. Multi-View Graph Clustering by Adaptive Manifold Learning. Mathematics, 2022, in press. [Link] [Source Code] [IF2022: 2.592]
  11. Qian Zhang, Zhao Kang, Zenglin Xu, Shudong Huang, Hongguang Fu. Spaks: Self-paced Multiple Kernel Subspace Clustering with Feature Smoothing Regularization. Knowledge-Based Systems, 2022, in press. [Link] [Source Code] [IF2022: 8.139]
  12. Quanhui Liu, Juanjuan Zhang, Cheng Peng, Maria Litvinova, Shudong Huang, and et.al. Model-based Evaluation of Alternative Reactive Class Closure Strategies Against COVID-19. Nature Communications, 2022, 13(1): 1-10. (nature 子刊) [Link] [Source Code] [IF2022: 17.694]

2021

  1. Shudong Huang, Ivor W. Tsang, Zenglin Xu, and Jiancheng Lv. Measuring Diversity in Graph Learning: A Unified Framework for Structured Multi-view Clustering. IEEE Transactions on Knowledge and Data Engineering, 2021, 34(12): 5869-5883. (CCF-A) [Link] [Source Code] (ESI Highly Cited Paper) [IF2021: 6.977]
  2. Shudong Huang, Zhao Kang, Zenglin Xu, and Quanhui Liu. Robust Deep K-Means: An Effective and Simple Method for Data Clustering. Pattern Recognition, 2021, in press. [Link] [Source Code] [IF2021: 7.74]
  3. Shudong Huang, Ivor W. Tsang, Zenglin Xu, Jiancheng Lv, and Quanhui Liu. CDD: Multi-view Subspace Clustering via Cross-view Diversity Detection. In: Proceedings of the 29th ACM International Conference on Multimedia (ACM MM’21), Chengdu, China, 2021, pp: 2308-2316. (CCF-A) [Link] [Source Code]
  4. Ji Zhang, Hongjun Wang, Shudong Huang, Tianrun Li, Peng Jin, Ping Deng, and Qigang Zhao. Co-Adjustment Learning for Co-Clustering. Cognitive Computation, 2021, 13: 504-517. [Link] [Source Code] [IF2021: 5.418]

2020

  1. Shudong Huang, Zhao Kang, and Zenglin Xu. Auto-weighted Multi-view Clustering via Deep Matrix Decomposition. Pattern Recognition, 2020, in press. [Link] [Source Code] [IF2020: 7.196]
  2. Shudong Huang, Zenglin Xu, Ivor W. Tsang, and Zhao Kang. Auto-weighted Multi-view Co-clustering with Bipartite Graphs. Information Sciences, 2020, 512: 18-30. [Link] [Source Code] [IF2020: 5.91]
  3. Shudong Huang, Zhao Kang, and Zenglin Xu. Deep K-Means: A Simple and Effective Method for Data Clustering. In: Proceedings of the International Conference on Neural Computing for Advanced Applications (NCAA’20), Shenzhen, China, 2020, pp: 272-283. [Link] [Source Code]
  4. Shudong Huang, Zenglin Xu, Zhao Kang, and Yazhou Ren. Regularized Nonnegative Matrix Factorization with Adaptive Local Structure Learning. Neurocomputing, 2020, 382: 196-209. [Link] [Source Code] [IF2020: 4.438]
  5. Zhao Kang, Guoxin Shi, Shudong Huang, Wenyu Chen, Xiaorong Pu, Joey Tianyi Zhou, and Zenglin Xu. Multi-graph Fusion for Multi-view Spectral Clustering. Knowledge-Based Systems, 2020, in press. [Link] [Source Code] (ESI Highly Cited Paper) [IF2020: 5.921]
  6. Yazhou Ren, Shudong Huang, Peng Zhao, Minghao Han, and Zenglin Xu. Self-paced and Auto-weighted Multi-view Clustering. Neurocomputing, 2020, 383: 248-256. [Link] [Source Code] [IF2020: 4.438]

2019

  1. Shudong Huang, Zhao Kang, Ivor W. Tsang, and Zenglin Xu. Auto-weighted Multi-view Clustering via Kernelized Graph Learning. Pattern Recognition, 2019, 88: 174-184. [Link] [Source Code] (ESI Highly Cited Paper) [IF2019: 5.898]
  2. Shudong Huang, Peng Zhao, Yazhou Ren, Tianrui Li, and Zenglin Xu. Self-paced and Soft-weighted Nonnegative Matrix Factorization for Data Representation. Knowledge-Based Systems, 2019, 164: 29-37, 2019. [Link] [Source Code] [IF2019: 5.101]
  3. Zhao Kang, Zipeng Guo, Shudong Huang, Siying Wang, Wenyu Chen, Yuanzhang Su, and Zenglin Xu. Multiple Partitions Aligned Clustering. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19), Macao, China, 2019, pp: 2701-2707. (CCF-A) [Link] [Source Code]
  4. Ji Zhang, Hongjun Wang, Jielei Chu, Shudong Huang, Tianrui Li, and Qigang Zhao. Improved Gaussian–Bernoulli Restricted Boltzmann Machine for Learning Discriminative Representations. Knowledge-Based Systems, 2019, in press. [Link] [Source Code] [IF2019: 5.101]

2018

  1. Shudong Huang, Yazhou Ren, and Zenglin Xu. Robust Multi-view Data Clustering with Multi-view Capped-norm K-means. Neurocomputing, 2018, 311: 197-208.[Link] [Source Code] [IF2018: 3.241]
  2. Shudong Huang, Zhao Kang, and Zenglin Xu. Self-weighted Multi-view Clustering with Soft Capped Norm. Knowledge-Based Systems, 2018, 158: 1-8. [Link] [Source Code] [IF2018: 4.396]
  3. Shudong Huang, Zenglin Xu, and Jiancheng Lv. Adaptive Local Structure Learning for Document Co-clustering. Knowledge-Based Systems, 2018, 148: 74-84. [Link] [Source Code] [IF2018: 4.396]
  4. Shudong Huang, Hongjun Wang, Tao Li, Taoianrui Li, and Zenglin Xu. Robust Graph Regularized Nonnegative Matrix Factorization for Clustering. Data Mining and Knowledge Discovery, 2018, 32 (2): 483-503. [Link] [Source Code] [IF2018: 2.481]

2017 and before

  1. Shudong Huang, Zenglin Xu, and Fei Wang. Nonnegative Matrix Factorization with Adaptive Neighbors. In: Proceedings of the 30th International Joint Conference on Neural Networks (IJCNN’17), Anchorage, Alaska, 2017, pp: 486-493. [Link] [Source Code]
  2. Shudong Huang, Hongjun Wang, Tao Li, Yan Yang, and Tianrui Li. Constraint Co-projections for Semi-supervised Co-clustering. IEEE Transactions on Cybernetics, 2016, 46(12): 3047-3058. [Link] [Source Code] [IF2016: 4.934]
  3. Shudong Huang, Hongjun Wang, Dingcheng Li, Yan Yang, and Tianrui Li. Spectral Co-clustering Ensemble. Knowledge-Based Systems, 2015, 84: 46-55. [Link] [Source Code] [IF2015: 2.947]