A new formulation of linear discriminant analysis for robust dimensionality reduction H Zhao, Z Wang, F Nie IEEE Transactions on Knowledge and Data Engineering 31 (4), 629-640, 2018 | 95 | 2018 |
Towards Robust Discriminative Projections Learning via Non-Greedy -Norm MinMax F Nie, Z Wang, R Wang, Z Wang, X Li IEEE transactions on pattern analysis and machine intelligence 43 (6), 2086-2100, 2019 | 69 | 2019 |
Discriminative Feature Selection via A Structured Sparse Subspace Learning Module Z Wang, F Nie, L Tian, R Wang, X Li IJCAI 2020, 2020 | 67 | 2020 |
Submanifold-preserving discriminant analysis with an auto-optimized graph F Nie, Z Wang, R Wang, X Li IEEE Transactions on Cybernetics, 2019 | 60 | 2019 |
Subspace Sparse Discriminative Feature Selection F Nie, Z Wang, L Tian, R Wang, X Li IEEE Transactions on Cybernetics, 2020 | 56 | 2020 |
Adaptive local linear discriminant analysis F Nie, Z Wang, R Wang, Z Wang, X Li ACM Transactions on Knowledge Discovery from Data (TKDD) 14 (1), 1-19, 2020 | 55 | 2020 |
Orthogonal least squares regression for feature extraction H Zhao, Z Wang, F Nie Neurocomputing 216, 200-207, 2016 | 50 | 2016 |
Curriculum audiovisual learning D Hu, Z Wang, H Xiong, D Wang, F Nie, D Dou arXiv preprint arXiv:2001.09414, 2020 | 41 | 2020 |
Adaptive local embedding learning for semi-supervised dimensionality reduction F Nie, Z Wang, R Wang, X Li IEEE Transactions on Knowledge and Data Engineering 34 (10), 4609-4621, 2021 | 33 | 2021 |
Local structured feature learning with dynamic maximum entropy graph Z Wang, F Nie, R Wang, H Yang, X Li Pattern Recognition 111, 107673, 2020 | 26 | 2020 |
Joint anchor graph embedding and discrete feature scoring for unsupervised feature selection Z Wang, D Wu, R Wang, F Nie, F Wang IEEE Transactions on Neural Networks and Learning Systems, 2022 | 17 | 2022 |
Capped -Norm LDA for Outliers Robust Dimension Reduction Z Wang, F Nie, C Zhang, R Wang, X Li IEEE Signal Processing Letters 27, 1315-1319, 2020 | 17 | 2020 |
Sparse and flexible projections for unsupervised feature selection R Wang, C Zhang, J Bian, Z Wang, F Nie, X Li IEEE Transactions on Knowledge and Data Engineering, 2022 | 13 | 2022 |
Multiclass Classification and Feature Selection Based on Least Squares Regression with Large Margin ZW Haifeng Zhao, Siqi Wang Neural computation 30 (10), 2781-2804, 2018 | 13 | 2018 |
Sparse trace ratio LDA for supervised feature selection Z Li, F Nie, D Wu, Z Wang, X Li IEEE transactions on cybernetics, 2023 | 10 | 2023 |
Joint adaptive graph learning and discriminative analysis for unsupervised feature selection H Zhao, Q Li, Z Wang, F Nie Cognitive Computation 14 (3), 1211-1221, 2022 | 10 | 2022 |
Fast local representation learning via adaptive anchor graph for image retrieval C Zhang, F Nie, Z Wang, R Wang, X Li Information Sciences 578, 870-886, 2021 | 9 | 2021 |
Adaptive neighborhood minmax projections H Zhao, Z Wang, F Nie Neurocomputing 313, 155-166, 2018 | 9 | 2018 |
Fuzzy c-multiple-means clustering for hyperspectral image X Yang, M Zhu, B Sun, Z Wang, F Nie IEEE Geoscience and Remote Sensing Letters 20, 1-5, 2023 | 8 | 2023 |
Fast spectral clustering with self-adapted bipartite graph learning X Yang, M Zhu, Y Cai, Z Wang, F Nie Information Sciences 644, 118810, 2023 | 7 | 2023 |