A survey on deep semi-supervised learning X Yang, Z Song, I King, Z Xu IEEE Transactions on Knowledge and Data Engineering 35 (9), 8934-8954, 2023 | 525 | 2023 |
Discriminative semi-supervised feature selection via manifold regularization Z Xu, I King, MRT Lyu, R Jin IEEE Transactions on Neural networks 21 (7), 1033-1047, 2010 | 433 | 2010 |
知识图谱技术综述 徐增林, 盛泳潘, 贺丽荣, 王雅芳 电子科技大学学报 45 (4), 589-606, 2016 | 353* | 2016 |
Simple and efficient multiple kernel learning by group lasso Z Xu, R Jin, H Yang, I King, MR Lyu Proceedings of the 27th international conference on machine learning (ICML …, 2010 | 339 | 2010 |
Large-scale multi-view subspace clustering in linear time Z Kang, W Zhou, Z Zhao, J Shao, M Han, Z Xu Proceedings of the AAAI conference on artificial intelligence 34 (04), 4412-4419, 2020 | 320 | 2020 |
Superneurons: Dynamic GPU memory management for training deep neural networks L Wang, J Ye, Y Zhao, W Wu, A Li, SL Song, Z Xu, T Kraska Proceedings of the 23rd ACM SIGPLAN symposium on principles and practice of …, 2018 | 281 | 2018 |
Robust graph learning from noisy data Z Kang, H Pan, SCH Hoi, Z Xu IEEE transactions on cybernetics 50 (5), 1833-1843, 2020 | 279 | 2020 |
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 | 250 | 2020 |
Partition level multiview subspace clustering Z Kang, X Zhao, C Peng, H Zhu, JT Zhou, X Peng, W Chen, Z Xu Neural Networks 122, 279-288, 2020 | 222 | 2020 |
An extended level method for efficient multiple kernel learning Z Xu, R Jin, I King, M Lyu Advances in neural information processing systems 21, 2008 | 213 | 2008 |
Auto-weighted multi-view clustering via kernelized graph learning S Huang, Z Kang, IW Tsang, Z Xu Pattern Recognition 88, 174-184, 2019 | 202 | 2019 |
Graph-based semi-supervised learning: A comprehensive review Z Song, X Yang, Z Xu, I King IEEE Transactions on Neural Networks and Learning Systems 34 (11), 8174-8194, 2022 | 187 | 2022 |
Semi-supervised deep embedded clustering Y Ren, K Hu, X Dai, L Pan, SCH Hoi, Z Xu Neurocomputing 325, 121-130, 2019 | 181 | 2019 |
Auto-weighted multi-view clustering via deep matrix decomposition S Huang, Z Kang, Z Xu Pattern Recognition 97, 107015, 2020 | 167 | 2020 |
Low-rank kernel learning for graph-based clustering Z Kang, L Wen, W Chen, Z Xu Knowledge-Based Systems 163, 510-517, 2019 | 167 | 2019 |
Infinite Tucker decomposition: Nonparametric Bayesian models for multiway data analysis Z Xu, F Yan, Y Qi Proceedings of the 29th International Conference on Machine Learning(ICML-12 …, 2012 | 156 | 2012 |
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition J Ye, L Wang, G Li, D Chen, S Zhe, X Chu, Z Xu The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 | 151 | 2018 |
Bayesian network technologies: applications and graphical models: applications and graphical models A Mittal, A Kassim IGI global, 2007 | 143 | 2007 |
Deep embedded multi-view clustering with collaborative training J Xu, Y Ren, G Li, L Pan, C Zhu, Z Xu Information Sciences 573, 279-290, 2021 | 129 | 2021 |
Online learning for group lasso H Yang, Z Xu, I King, MR Lyu Proceedings of the 27th International Conference on Machine Learning (ICML …, 2010 | 122 | 2010 |