| Local and global structure preservation for robust unsupervised spectral feature selection X Zhu, S Zhang, R Hu, Y Zhu IEEE Transactions on Knowledge and Data Engineering 30 (3), 517-529, 2017 | 194 | 2017 |
| Unsupervised feature selection by self-paced learning regularization W Zheng, X Zhu, G Wen, Y Zhu, H Yu, J Gan Pattern recognition letters 132, 4-11, 2020 | 147 | 2020 |
| Interpretable learning based dynamic graph convolutional networks for alzheimer’s disease analysis Y Zhu, J Ma, C Yuan, X Zhu Information Fusion 77, 53-61, 2022 | 117 | 2022 |
| kNN Algorithm with Data-Driven k Value D Cheng, S Zhang, Z Deng, Y Zhu, M Zong Advanced Data Mining and Applications: 10th International Conference, ADMA …, 2014 | 112 | 2014 |
| Robust SVM with adaptive graph learning R Hu, X Zhu, Y Zhu, J Gan World Wide Web 23, 1945-1968, 2020 | 102 | 2020 |
| Dynamic graph learning for spectral feature selection W Zheng, X Zhu, Y Zhu, R Hu, C Lei Multimedia tools and applications 77, 29739-29755, 2018 | 94 | 2018 |
| Spectral rotation for deep one-step clustering X Zhu, Y Zhu, W Zheng Pattern Recognition 105, 107175, 2020 | 77 | 2020 |
| Unsupervised spectral feature selection with dynamic hyper-graph learning X Zhu, S Zhang, Y Zhu, P Zhu, Y Gao IEEE Transactions on Knowledge and Data Engineering 34 (6), 3016-3028, 2020 | 73 | 2020 |
| Half-quadratic minimization for unsupervised feature selection on incomplete data HT Shen, Y Zhu, W Zheng, X Zhu IEEE transactions on neural networks and learning systems 32 (7), 3122-3135, 2020 | 66 | 2020 |
| Multi-view classification for identification of Alzheimer’s disease X Zhu, HI Suk, Y Zhu, KH Thung, G Wu, D Shen Machine Learning in Medical Imaging: 6th International Workshop, MLMI 2015 …, 2015 | 50 | 2015 |
| Multigraph fusion for dynamic graph convolutional network J Gan, R Hu, Y Mo, Z Kang, L Peng, Y Zhu, X Zhu IEEE Transactions on Neural Networks and Learning Systems, 2022 | 48 | 2022 |
| Self-weighted multi-view fuzzy clustering X Zhu, S Zhang, Y Zhu, W Zheng, Y Yang ACM transactions on knowledge discovery from data (TKDD) 14 (4), 1-17, 2020 | 39 | 2020 |
| Multi-band brain network analysis for functional neuroimaging biomarker identification R Hu, Z Peng, X Zhu, J Gan, Y Zhu, J Ma, G Wu IEEE transactions on medical imaging 40 (12), 3843-3855, 2021 | 37 | 2021 |
| Adaptive Hypergraph Learning for Unsupervised Feature Selection. X Zhu, Y Zhu, S Zhang, R Hu, W He IJCAI, 3581-3587, 2017 | 35 | 2017 |
| Feature self-representation based hypergraph unsupervised feature selection via low-rank representation W He, X Cheng, R Hu, Y Zhu, G Wen Neurocomputing 253, 127-134, 2017 | 26 | 2017 |
| One-step spectral clustering via dynamically learning affinity matrix and subspace X Zhu, W He, Y Li, Y Yang, S Zhang, R Hu, Y Zhu Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 26 | 2017 |
| A novel low-rank hypergraph feature selection for multi-view classification X Cheng, Y Zhu, J Song, G Wen, W He Neurocomputing 253, 115-121, 2017 | 23 | 2017 |
| One-step spectral rotation clustering for imbalanced high-dimensional data G Wen, X Li, Y Zhu, L Chen, Q Luo, M Tan Information Processing & Management 58 (1), 102388, 2021 | 21 | 2021 |
| Multi-view multi-sparsity kernel reconstruction for multi-class image classification X Zhu, Q Xie, Y Zhu, X Liu, S Zhang Neurocomputing 169, 43-49, 2015 | 19 | 2015 |
| Self-representation and PCA embedding for unsupervised feature selection Y Zhu, X Zhang, R Wang, W Zheng, Y Zhu World Wide Web 21, 1675-1688, 2018 | 18 | 2018 |
