| Auto-weighted multi-view clustering via kernelized graph learning S Huang, Z Kang, IW Tsang, Z Xu Pattern Recognition 88, 174-184, 2019 | 109 | 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 | 91 | 2020 |
| Auto-weighted multi-view clustering via deep matrix decomposition S Huang, Z Kang, Z Xu Pattern Recognition 97, 107015, 2020 | 79 | 2020 |
| Self-weighted multi-view clustering with soft capped norm S Huang, Z Kang, Z Xu Knowledge-Based Systems 158, 1-8, 2018 | 61 | 2018 |
| Robust multi-view data clustering with multi-view capped-norm k-means S Huang, Y Ren, Z Xu Neurocomputing 311, 197-208, 2018 | 55 | 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 | 49 | 2018 |
| Spectral co-clustering ensemble S Huang, H Wang, D Li, Y Yang, T Li Knowledge-Based Systems 84, 46-55, 2015 | 48 | 2015 |
| Adaptive local structure learning for document co-clustering S Huang, Z Xu, J Lv Knowledge-Based Systems 148, 74-84, 2018 | 43 | 2018 |
| Auto-weighted multi-view co-clustering with bipartite graphs S Huang, Z Xu, IW Tsang, Z Kang Information Sciences 512, 18-30, 2020 | 38 | 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 | 31 | 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 | 28 | 2021 |
| Self-paced and auto-weighted multi-view clustering Y Ren, S Huang, P Zhao, M Han, Z Xu Neurocomputing 383, 248-256, 2020 | 22 | 2020 |
| Regularized nonnegative matrix factorization with adaptive local structure learning S Huang, Z Xu, Z Kang, Y Ren Neurocomputing 382, 196-209, 2020 | 20 | 2020 |
| 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 | 19 | 2021 |
| Nonnegative matrix factorization with adaptive neighbors S Huang, Z Xu, F Wang 2017 International Joint Conference on Neural Networks (IJCNN), 486-493, 2017 | 18 | 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 | 16 | 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 | 15 | 2019 |
| 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 | 13 | 2019 |
| Model-based evaluation of alternative reactive class closure strategies against COVID-19 QH Liu, J Zhang, C Peng, M Litvinova, S Huang, P Poletti, F Trentini, ... Nature communications 13 (1), 1-10, 2022 | 8 | 2022 |
| CDD: Multi-view Subspace Clustering via Cross-view Diversity Detection S Huang, IW Tsang, Z Xu, J Lv, Q Liu Proceedings of the 29th ACM International Conference on Multimedia, 2308-2316, 2021 | 2 | 2021 |
Zenglin XuHarbin Institute of Technology, Shenzhen & Peng Cheng LabVerified email at hit.edu.cn
