| Learning sparse svm for feature selection on very high dimensional datasets M Tan, L Wang, IW Tsang Proceedings of the 27th international conference on machine learning (ICML …, 2010 | 177 | 2010 |
| Gene selection using hybrid particle swarm optimization and genetic algorithm S Li, X Wu, M Tan Soft Computing 12 (11), 1039-1048, 2008 | 145 | 2008 |
| Towards ultrahigh dimensional feature selection for big data M Tan, IW Tsang, L Wang The Journal of Machine Learning Research 15 (1), 1371-1429, 2014 | 134 | 2014 |
| Heterogeneous domain adaptation for multiple classes JT Zhou, IW Tsang, SJ Pan, M Tan Artificial Intelligence and Statistics, 1095-1103, 2014 | 86 | 2014 |
| A hybrid PSO-BFGS strategy for global optimization of multimodal functions S Li, M Tan, IW Tsang, JTY Kwok IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41 …, 2011 | 78 | 2011 |
| Robust kernel low-rank representation S Xiao, M Tan, D Xu, ZY Dong IEEE transactions on neural networks and learning systems 27 (11), 2268-2281, 2015 | 65 | 2015 |
| Weighted block-sparse low rank representation for face clustering in videos S Xiao, M Tan, D Xu European Conference on Computer Vision, 123-138, 2014 | 63 | 2014 |
| Discrimination-aware channel pruning for deep neural networks Z Zhuang, M Tan, B Zhuang, J Liu, Y Guo, Q Wu, J Huang, J Zhu Advances in Neural Information Processing Systems, 875-886, 2018 | 57 | 2018 |
| Tuning SVM parameters by using a hybrid CLPSO–BFGS algorithm S Li, M Tan Neurocomputing 73 (10-12), 2089-2096, 2010 | 51 | 2010 |
| Riemannian pursuit for big matrix recovery M Tan, IW Tsang, L Wang, B Vandereycken, SJ Pan International Conference on Machine Learning, 1539-1547, 2014 | 47 | 2014 |
| Blind image deconvolution by automatic gradient activation D Gong, M Tan, Y Zhang, A Van den Hengel, Q Shi Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 45 | 2016 |
| Towards effective low-bitwidth convolutional neural networks B Zhuang, C Shen, M Tan, L Liu, I Reid Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 41 | 2018 |
| ML-FOREST: A multi-label tree ensemble method for multi-label classification Q Wu, M Tan, H Song, J Chen, MK Ng IEEE transactions on knowledge and data engineering 28 (10), 2665-2680, 2016 | 39 | 2016 |
| Learning graph structure for multi-label image classification via clique generation M Tan, Q Shi, A van den Hengel, C Shen, J Gao, F Hu, Z Zhang Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 32 | 2015 |
| Compressive representation for device-free activity recognition with passive RFID signal strength L Yao, QZ Sheng, X Li, T Gu, M Tan, X Wang, S Wang, W Ruan IEEE Transactions on Mobile Computing 17 (2), 293-306, 2017 | 30 | 2017 |
| Matching pursuit LASSO part I: Sparse recovery over big dictionary M Tan, IW Tsang, L Wang IEEE Transactions on Signal Processing 63 (3), 727-741, 2014 | 30 | 2014 |
| Minimax sparse logistic regression for very high-dimensional feature selection M Tan, IW Tsang, L Wang IEEE transactions on neural networks and learning systems 24 (10), 1609-1622, 2013 | 30 | 2013 |
| Visual grounding via accumulated attention C Deng, Q Wu, Q Wu, F Hu, F Lyu, M Tan Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 27 | 2018 |
| Discovering support and affiliated features from very high dimensions Y Zhai, M Tan, I Tsang, YS Ong arXiv preprint arXiv:1206.6477, 2012 | 26 | 2012 |
| Fast algorithms for linear and kernel svm+ W Li, D Dai, M Tan, D Xu, L Van Gool Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 25 | 2016 |
Ivor W. TsangResearch Director & Professor of AI, Centre for AI, University of Technology SydneyVerified email at uts.edu.au
