| Attribute-enhanced face recognition with neural tensor fusion networks G Hu, Y Hua, Y Yuan, Z Zhang, Z Lu, SS Mukherjee, TM Hospedales, ... Proceedings of the IEEE International Conference on Computer Vision, 3744-3753, 2017 | 90 | 2017 |
| Joint hypergraph learning and sparse regression for feature selection Z Zhang, L Bai, Y Liang, E Hancock Pattern Recognition 63, 291-309, 2017 | 75 | 2017 |
| A graph-based approach to feature selection Z Zhang, ER Hancock International workshop on graph-based representations in pattern recognition …, 2011 | 68 | 2011 |
| Semisupervised hyperspectral band selection via spectral–spatial hypergraph model X Bai, Z Guo, Y Wang, Z Zhang, J Zhou IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2015 | 66 | 2015 |
| Quantum-based subgraph convolutional neural networks Z Zhang, D Chen, J Wang, L Bai, ER Hancock Pattern Recognition 88, 38-49, 2019 | 63 | 2019 |
| Deep stock representation learning: From candlestick charts to investment decisions G Hu, Y Hu, K Yang, Z Yu, F Sung, Z Zhang, F Xie, J Liu, N Robertson, ... 2018 IEEE international conference on acoustics, speech and signal …, 2018 | 63 | 2018 |
| Learning EEG topographical representation for classification via convolutional neural network M Xu, J Yao, Z Zhang, R Li, B Yang, C Li, J Li, J Zhang Pattern Recognition 105, 107390, 2020 | 60 | 2020 |
| An aligned subtree kernel for weighted graphs L Bai, L Rossi, Z Zhang, E Hancock International Conference on Machine Learning, 30-39, 2015 | 60 | 2015 |
| Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition L Zhou, Q Mao, X Huang, F Zhang, Z Zhang Pattern Recognition 122, 108275, 2022 | 59 | 2022 |
| Deep multi-task learning to recognise subtle facial expressions of mental states G Hu, L Liu, Y Yuan, Z Yu, Y Hua, Z Zhang, F Shen, L Shao, ... Proceedings of the European conference on computer vision (ECCV), 103-119, 2018 | 51 | 2018 |
| Hypergraph based information-theoretic feature selection Z Zhang, ER Hancock Pattern Recognition Letters 33 (15), 1991-1999, 2012 | 47 | 2012 |
| Face frontalization using an appearance-flow-based convolutional neural network Z Zhang, X Chen, B Wang, G Hu, W Zuo, ER Hancock IEEE Transactions on Image Processing 28 (5), 2187-2199, 2018 | 45 | 2018 |
| Entropic dynamic time warping kernels for co-evolving financial time series analysis L Bai, L Cui, Z Zhang, L Xu, Y Wang, ER Hancock IEEE Transactions on Neural Networks and Learning Systems 34 (4), 1808-1822, 2020 | 37 | 2020 |
| Quantum kernels for unattributed graphs using discrete-time quantum walks L Bai, L Rossi, L Cui, Z Zhang, P Ren, X Bai, E Hancock Pattern Recognition Letters 87, 96-103, 2017 | 36 | 2017 |
| Depth-based subgraph convolutional auto-encoder for network representation learning Z Zhang, D Chen, Z Wang, H Li, L Bai, ER Hancock Pattern Recognition 90, 363-376, 2019 | 31 | 2019 |
| High-order covariate interacted Lasso for feature selection Z Zhang, Y Tian, L Bai, J Xiahou, E Hancock Pattern Recognition Letters 87, 139-146, 2017 | 29 | 2017 |
| H-Net: Neural Network for Cross-domain Image Patch Matching. W Liu, X Shen, C Wang, Z Zhang, C Wen, J Li IJCAI, 856-863, 2018 | 28 | 2018 |
| A hypergraph based semi-supervised band selection method for hyperspectral image classification Z Guo, X Bai, Z Zhang, J Zhou 2013 IEEE International Conference on Image Processing, 3137-3141, 2013 | 28 | 2013 |
| A Graph Kernel based on Jensen-Shannon Representation L Bai, Z Zhang, ER Hancock International Joint Conference on Artificial Intelligence (IJCAI), 2015 | 23 | 2015 |
| Semi-supervised hyperspectral band selection via sparse linear regression and hypergraph models Z Guo, H Yang, X Bai, Z Zhang, J Zhou 2013 IEEE international geoscience and remote sensing symposium-IGARSS, 1474 …, 2013 | 22 | 2013 |
