Recursive projection twin support vector machine via within-class variance minimization X Chen, J Yang, Q Ye, J Liang Pattern Recognition 44 (10-11), 2643-2655, 2011 | 194 | 2011 |
Multiview learning with robust double-sided twin SVM Q Ye, P Huang, Z Zhang, Y Zheng, L Fu, W Yang IEEE transactions on Cybernetics 52 (12), 12745-12758, 2021 | 127 | 2021 |
L1-norm distance linear discriminant analysis based on an effective iterative algorithm Q Ye, J Yang, F Liu, C Zhao, N Ye, T Yin IEEE Transactions on Circuits and Systems for Video Technology, 2018 | 107 | 2018 |
Nonpeaked discriminant analysis for data representation Q Ye, Z Li, L Fu, Z Zhang, W Yang, G Yang IEEE transactions on neural networks and learning systems 30 (12), 3818-3832, 2019 | 106 | 2019 |
Learning Robust Discriminant Subspace Based on Joint L₂,ₚ- and L₂,ₛ-Norm Distance Metrics L Fu, Z Li, Q Ye, H Yin, Q Liu, X Chen, X Fan, W Yang, G Yang IEEE transactions on neural networks and learning systems 33 (1), 130-144, 2020 | 105 | 2020 |
L1-Norm Distance Minimization-Based Fast Robust Twin Support Vector -Plane Clustering Q Ye, H Zhao, Z Li, X Yang, S Gao, T Yin, N Ye IEEE transactions on neural networks and learning systems 29 (9), 4494-4503, 2017 | 99 | 2017 |
Least squares twin bounded support vector machines based on L1-norm distance metric for classification H Yan, Q Ye, T Zhang, DJ Yu, X Yuan, Y Xu, L Fu Pattern recognition 74, 434-447, 2018 | 88 | 2018 |
Weighted twin support vector machines with local information and its application Q Ye, C Zhao, S Gao, H Zheng Neural Networks 35, 31-39, 2012 | 81 | 2012 |
Recursive robust least squares support vector regression based on maximum correntropy criterion X Chen, J Yang, J Liang, Q Ye Neurocomputing 97, 63-73, 2012 | 76 | 2012 |
Analysis of the complete mitochondrial genome sequence of the diploid cotton Gossypium raimondii by comparative genomics approaches C Bi, AH Paterson, X Wang, Y Xu, D Wu, Y Qu, A Jiang, Q Ye, N Ye BioMed Research International 2016, 2016 | 71 | 2016 |
Multi-weight vector projection support vector machines Q Ye, C Zhao, N Ye, Y Chen Pattern Recognition Letters 31 (13), 2006-2011, 2010 | 71 | 2010 |
1-Norm least squares twin support vector machines S Gao, Q Ye, N Ye Neurocomputing 74 (17), 3590-3597, 2011 | 70 | 2011 |
Smooth twin support vector regression X Chen, J Yang, J Liang, Q Ye Neural Computing and Applications 21, 505-513, 2012 | 63 | 2012 |
Recurrent thrifty attention network for remote sensing scene recognition L Fu, D Zhang, Q Ye IEEE Transactions on Geoscience and Remote Sensing 59 (10), 8257-8268, 2020 | 60 | 2020 |
Robust blood pressure estimation using an RGB camera X Fan, Q Ye, X Yang, SD Choudhury Journal of Ambient Intelligence and Humanized Computing 11, 4329-4336, 2020 | 59 | 2020 |
Assembly and comparative analysis of complete mitochondrial genome sequence of an economic plant Salix suchowensis N Ye, X Wang, J Li, C Bi, Y Xu, D Wu, Q Ye PeerJ 5, e3148, 2017 | 58 | 2017 |
Lp-and Ls-norm distance based robust linear discriminant analysis Q Ye, L Fu, Z Zhang, H Zhao, M Naiem Neural Networks 105, 393-404, 2018 | 57 | 2018 |
Organellar genome assembly methods and comparative analysis of horticultural plants X Wang, F Cheng, D Rohlsen, C Bi, C Wang, Y Xu, S Wei, Q Ye, T Yin, ... Horticulture research 5, 2018 | 55 | 2018 |
Robust capped L1-norm twin support vector machine C Wang, Q Ye, P Luo, N Ye, L Fu Neural Networks 114, 47-59, 2019 | 51 | 2019 |
Localized twin SVM via convex minimization Q Ye, C Zhao, N Ye, X Chen Neurocomputing 74 (4), 580-587, 2011 | 46 | 2011 |