Shu-Dong Huang

Shu-Dong Huang

Associate Professor of Computer Science, Sichuan University

Selected Publications

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

Manuscript/Preprints

  1. Wentao Feng#, Shu-Dong Huang#, Jianfeng Liu, and Jiancheng Lv. Deep Neural Networks Meet Partial Differential Equations: A Comprehensive Survey.

2026

  1. Zhuoyao Liu, Yang Liu, Wentao Feng, and Shudong Huang*. PCSR: Pseudo-label Consistency-Guided Sample Refinement for Noisy Correspondence Learning. In: Proceedings of the 40th AAAI Conference on Artificial Intelligence (AAAI’26), Singapore, 2026, to appear. (CCF-A) [Link] [Source Code] [D.o.C: 20–27. Jan. 2026, Issue Date: –]
  2. Ping He, Rong Xiao, Wanying Xu, Chenwei Tang, Shudong Huang, and Huajin Tang. S3Net: Spatiotemporally Separated Sparse Network for Neuromorphic Vision Processing. In: Proceedings of the 40th AAAI Conference on Artificial Intelligence (AAAI’26), Singapore, 2026, to appear. (CCF-A) [Link] [Source Code] [D.o.C: 20–27. Jan. 2026, Issue Date: –]
  3. Ao Hu, Liangjian Wen, Jiang Duan, Yong Dai, Dongkai Wang, Shudong Huang, Jun Wang, and Zenglin Xu. FDNet: High-frequency Disentanglement Network with Information-theoretic Guidance for Multivariate Time Series Forecasting. Pattern Recognition, 2026, 173: 11 pages. [Link] [Source Code] [IF2025: 7.6] [Issue Date: May 2026]

2025

  1. Yifan Wang, Tao Wang, Chenwei Tang, Caiyang Yu, Zhengqing Zang, Mengmi Zhang, Shudong Huang, and Jiancheng Lv. Dual Prompt Learning for Adapting Vision-Language Models to Downstream Image-Text Retrieval. In: Proceedings of the 33rd ACM International Conference on Multimedia (ACM MM’25), Dublin, lreland, 2025, pp: 5922-5931. (CCF-A) [Link] [Source Code] [D.o.C: 27–31. Oct. 2025, Issue Date: 27. Oct. 2025]
  2. Yang Liu, Wentao Feng, Zhuoyao Liu, Shudong Huang*, and Jiancheng Lv. Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text matching. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’25), Honolulu, Hawai’i, 2025, pp: 21679-21688. (CCF-A) [Link] [Source Code] [D.o.C: 19–23. Oct. 2025, Issue Date: 14. Oct. 2025]
  3. Wentao Feng, Ming Hu, Rui Huang, Chenwei Tang, Shudong Huang*, and Jiancheng Lv. DSAIS-PINN: Dynamic Seeds Allocation Importance Sampling for Physics-Informed Neural Networks. Neurocomputing, 2025, 648: 14 papges. [Link] [Source Code] [IF2024: 6.5] [Issue Date: 01. Oct. 2025]
  4. Yuze Tan, Yanjie Gou, Kouying Xue, Shudong Huang#, Yi Hu#, Ivor W. Tsang, and Jiancheng Lv. Implicit Multi-Behavior Generative Recommendation with Mixture of Quantization. IEEE Transactions on Knowledge and Data Engineering, 2025, 37(8): 4704-4715. (CCF-A) [Link] [Source Code] [IF2024: 8.9] [Issue Date: Aug. 2025]
  5. Shudong Huang, Rui Huang, Ming Hu, Wentao Feng, and Jiancheng Lv. DONIS: Importance Sampling for Training Physics-Informed DeepONet. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI’25), Montreal, Canada, 2025, pp: 5417-5425. (CCF-A) [Link] [Source Code] [D.o.C: 29-31. Aug. 2025, Issue Date: Aug. 2025]
  6. Shudong Huang#, Wentao Feng#, Chenwei Tang, Zhenan He, Caiyang Yu, and Jiancheng Lv. Partial Differential Equations Meet Deep Neural Networks: A Survey. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(8): 13649-13669. [Link] [Source Code] [IF2024: 10.2] [Issue Date: Aug. 2025]
  7. Rong Xiao, Ping He, Chenwei Tang, Shudong Huang, Jiancheng Lv, and Huajin Tang. STSF: Spiking Time Sparse Feedback Learning for Spiking Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(6): 11479-11492. [Link] [Source Code] [IF2024: 10.2] [Issue Date: Jun. 2025]
  8. Yang Liu, Shudong Huang*, Deng Xiong, and Jiancheng Lv. Learning Dynamic Similarity By Bidirectional Hierarchical Sliding Semantic Probe For Efficient Text Video Retrieval. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI’25), Philadelphia, USA, 2025, 39(6): 5667-5675. (CCF-A) [Link] [Source Code] [D.o.C: 25. Feb.–04. Mar. 2025, Issue Date: 11. Apr. 2025]
  9. Yang Liu, Mengyuan Liu, Shudong Huang, and Jiancheng Lv. Asymmetric Visual Semantic Embedding Framework for Efficient Vision-Language Alignment. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI’25), Philadelphia, USA, 2025, 39(6): 5676-5684. (CCF-A) [Link] [Source Code] [D.o.C: 25. Feb.–04. Mar. 2025, Issue Date: 11. Apr. 2025]
  10. Peng Su, Shudong Huang*, Weihong Ma, Deng Xiong, and Jiancheng Lv. Multi-view Granular-ball Contrastive Clustering. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI’25), Philadelphia, USA, 2025, 39(19): 20637-20645. (CCF-A) [Link] [Source Code] [D.o.C: 25. Feb.–04. Mar. 2025, Issue Date: 11. Apr. 2025]
  11. Jianpeng Chen, Yawen Ling, Jie Xu, Yazhou Ren, Shudong Huang, Xiaorong Pu, Zhifeng Hao, Philip S. Yu, and Lifang He. Variational Graph Generator for Multi-View Graph Clustering. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(6): 11078-11091. [Link] [Source Code] [IF2024: 10.2] [Issue Date: Jun. 2025]

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, pp: 5299-5307. (CCF-A) [Link] [Source Code] [D.o.C: 28. Oct.-01. Nov. 2024, Issue Date: 28. Oct. 2024]
  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, 36(12): 8179-8191. (CCF-A) [Link] [Source Code] [IF2024: 8.9] [Issue Date: Dec. 2024]
  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] [D.o.C: 21-27. Jul. 2024, Issue Date: 21. Jul. 2024]
  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] [D.o.C: 03-09. Aug. 2024, Issue Date: Aug. 2024]
  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] [D.o.C: 03-09. Aug. 2024, Issue Date: Aug. 2024]
  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, 26: 9150-9162. [Link] [Source Code] [IF2024: 8.4] [Issue Date: 16. Apr. 2024]
  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] [D.o.C: 20–27. Feb. 2024, Issue Date: 24. Mar. 2024]
  8. 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, 2024, 35(3): 4206-4219. [Link] [Source Code] [IF2022: 14.255] [Issue Date: Mar. 2024]

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, 19(9): 20 pages. [Link] [Source Code] [IF2023: 4.3] [Issue Date: 01. Sep. 2023]
  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] [D.o.C: 10-16. Dec. 2023, Issue Date: Dec. 2023]
  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] [D.o.C: 29. Oct.-3. Nov. 2023, Issue Date: 27. Oct. 2023]
  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] [D.o.C: 29. Oct.-3. Nov. 2023, Issue Date: 27. Oct. 2023]
  5. Shudong Huang, Yixi Liu, Hecheng Cai, Yuze Tan, Chenwei Tang, and Jiancheng Lv. Smooth Representation Learning from Multi-view Data. Information Fusion, 2023, 100: 9 pages. [Link] [Source Code] [IF2023: 18.6] [Issue Date: Dec. 2023]
  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] [D.o.C: 19-25. Aug. 2023, Issue Date: Aug. 2023]
  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] [D.o.C: 18-22. Jun. 2023, Issue Date: Jun. 2023]
  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] [D.o.C: 7-14. Feb. 2023, Issue Date: 26. Jun. 2023]
  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] [D.o.C: 7-14. Feb. 2023, Issue Date: 26. Jun. 2023]
  10. Hongjie Wu, Shudong Huang*, Chenwei Tang, Yancheng Zhang, and Jiancheng Lv. Pure Graph-guided Multi-view Subspace Clustering. Pattern Recognition, 2023, 136: 12 pages. [Link] [Source Code] [IF2023: 8.0] [Issue Date: Apr. 2023]
  11. 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, 2023, 35(8): 8270-8281. (CCF-A) [Link] [Source Code] [IF2023: 9.235] [Issue Date: 01. Aug. 2023]
  12. Shudong Huang, Ivor W. Tsang, Zenglin Xu, and Jiancheng Lv. Latent Representation Guided Multi-view Clustering. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(7): 7082-7087. (CCF-A) [Link] [Source Code] [IF2023: 9.235] [Issue Date: 01. Jul. 2023]

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] [D.o.C: 28. Nov.-09. Dec. 2022, Issue Date: Dec. 2022]
  2. 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, 2022, 34(12): 5869-5883. (CCF-A) [Link] [Source Code] (ESI Highly Cited Paper) [IF2022: 9.235] [Issue Date: 01. Dec. 2022]
  3. 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] [D.o.C: 10-14. Oct. 2022, Issue Date: 10. Oct. 2022]
  4. 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] [D.o.C: 10-14. Oct. 2022, Issue Date: 10. Oct. 2022]
  5. Shudong Huang, Wei Shi, Zenglin Xu, Ivor W. Tsang, and Jiancheng Lv. Efficient federated multi-view learning. Pattern Recognition, 2022, 131: 10 pages. [Link] [Source Code] [IF2022: 8.518] [Issue Date: Nov. 2022]
  6. 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] [D.o.C: 22. Feb.–01. Mar. 2022, Issue Date: 28. Jun. 2022]
  7. 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] [Issue Date: Mar. 2022]
  8. Peng Zhao, Hongjie Wu, and Shudong Huang*. Multi-View Graph Clustering by Adaptive Manifold Learning. Mathematics, 2022, 10(11): 16 pages. [Link] [Source Code] [IF2022: 2.592] [Issue Date: 25. May. 2022]
  9. 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, 253: 10 pages. [Link] [Source Code] [IF2022: 8.139] [Issue Date: 11. Oct. 2022]
  10. 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: 10 pages. (nature 子刊) [Link] [Source Code] [IF2022: 17.694] [Issue Date: 14. Jan. 2022]

2021

  1. Shudong Huang, Zhao Kang, Zenglin Xu, and Quanhui Liu. Robust Deep K-Means: An Effective and Simple Method for Data Clustering. Pattern Recognition, 2021, 117: 10 pages. [Link] [Source Code] [IF2021: 7.74] [Issue Date: Sep. 2021]
  2. 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] [D.o.C: 20–24. Oct. 2021, Issue Date: 17. Oct. 2021]
  3. 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] [Issue Date: Mar. 2021]

2020

  1. Shudong Huang, Zhao Kang, and Zenglin Xu. Auto-weighted Multi-view Clustering via Deep Matrix Decomposition. Pattern Recognition, 2020, 97: 11 pages. [Link] [Source Code] [IF2020: 7.196] [Issue Date: Jan. 2020]
  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] [Issue Date: Feb. 2020]
  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] [D.o.C: 3–5. Jul. 2020, Issue Date: 13. Aug. 2020]
  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] [Issue Date: 21. Mar. 2020]
  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, 189: 9 pages. [Link] [Source Code] (ESI Highly Cited Paper) [IF2020: 5.921] [Issue Date: 15. Feb. 2020]
  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] [Issue Date: 28. Mar. 2020]

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] [Issue Date: Apr. 2019]
  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] [Issue Date: 15. Jan. 2019]
  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] [D.o.C: 10-16. Aug. 2019, Issue Date: 10. Aug. 2019]
  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, 185: 13 pages. [Link] [Source Code] [IF2019: 5.101] [Issue Date: 01. Dec. 2019]

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] [Issue Date: 15. Oct. 2018]
  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] [Issue Date: 15. Oct. 2018]
  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] [Issue Date: 15. May. 2018]
  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] [Issue Date: Mar. 2018]

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] [D.o.C: 14-19. May. 2017, Issue Date: 03. Jul. 2017]
  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] [Issue Date: Dec. 2016]
  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] [Issue Date: Aug. 2015]