| Auto-weighted multi-view clustering via kernelized graph learning S Huang, Z Kang, IW Tsang, Z Xu Pattern Recognition 88, 174-184, 2019 | 74 | 2019 |
| Multi-graph fusion for multi-view spectral clustering Z Kang, G Shi, S Huang, W Chen, X Pu, JT Zhou, Z Xu Knowledge-Based Systems 189, 105102, 2020 | 61 | 2020 |
| Self-weighted multi-view clustering with soft capped norm S Huang, Z Kang, Z Xu Knowledge-Based Systems 158, 1-8, 2018 | 49 | 2018 |
| Robust multi-view data clustering with multi-view capped-norm k-means S Huang, Y Ren, Z Xu Neurocomputing 311, 197-208, 2018 | 47 | 2018 |
| Spectral co-clustering ensemble S Huang, H Wang, D Li, Y Yang, T Li Knowledge-Based Systems 84, 46-55, 2015 | 45 | 2015 |
| Auto-weighted multi-view clustering via deep matrix decomposition S Huang, Z Kang, Z Xu Pattern Recognition 97, 107015, 2020 | 44 | 2020 |
| Adaptive local structure learning for document co-clustering S Huang, Z Xu, J Lv Knowledge-Based Systems 148, 74-84, 2018 | 40 | 2018 |
| Robust graph regularized nonnegative matrix factorization for clustering S Huang, H Wang, T Li, T Li, Z Xu Data Mining and Knowledge Discovery 32 (2), 483-503, 2018 | 40 | 2018 |
| Auto-weighted multi-view co-clustering with bipartite graphs S Huang, Z Xu, IW Tsang, Z Kang Information Sciences 512, 18-30, 2020 | 21 | 2020 |
| Self-paced and auto-weighted multi-view clustering Y Ren, S Huang, P Zhao, M Han, Z Xu Neurocomputing 383, 248-256, 2020 | 15 | 2020 |
| Multiple partitions aligned clustering Z Kang, Z Guo, S Huang, S Wang, W Chen, Y Su, Z Xu The 28th International Joint Conference on Artificial Intelligence (IJCAI …, 2019 | 15 | 2019 |
| Nonnegative matrix factorization with adaptive neighbors S Huang, Z Xu, F Wang 2017 International Joint Conference on Neural Networks (IJCNN), 486-493, 2017 | 15 | 2017 |
| Constraint co-projections for semi-supervised co-clustering S Huang, H Wang, T Li, Y Yang, T Li IEEE transactions on cybernetics 46 (12), 3047-3058, 2015 | 15 | 2015 |
| Self-paced and soft-weighted nonnegative matrix factorization for data representation S Huang, P Zhao, Y Ren, T Li, Z Xu Knowledge-Based Systems 164, 29-37, 2019 | 14 | 2019 |
| Regularized nonnegative matrix factorization with adaptive local structure learning S Huang, Z Xu, Z Kang, Y Ren Neurocomputing 382, 196-209, 2020 | 13 | 2020 |
| Improved Gaussian–Bernoulli restricted Boltzmann machine for learning discriminative representations J Zhang, H Wang, J Chu, S Huang, T Li, Q Zhao Knowledge-Based Systems 185, 104911, 2019 | 9 | 2019 |
| Robust deep k-means: An effective and simple method for data clustering S Huang, Z Kang, Z Xu, Q Liu Pattern Recognition 117, 107996, 2021 | 3 | 2021 |
| Measuring Diversity in Graph Learning: A Unified Framework for Structured Multi-view Clustering S Huang, I Tsang, Z Xu, JC Lv IEEE Transactions on Knowledge and Data Engineering, 2021 | 2 | 2021 |
| Deep K-Means: A Simple and Effective Method for Data Clustering S Huang, Z Kang, Z Xu International Conference on Neural Computing for Advanced Applications, 272-283, 2020 | 2 | 2020 |
| Iterative Orthogonal Federated Multi-view Learning S Huang, W Shi, Z Xu, IW Tsang preprint http://smilelab. uestc. edu. cn/members/huangshudong/Iterative …, 2019 | 1 | 2019 |
Zenglin XuHarbin Institute of Technology, ShenzhenVerified email at hit.edu.cn
