| An unsupervised parameter learning model for RVFL neural network Y Zhang, J Wu, Z Cai, B Du, SY Philip Neural Networks 112, 85-97, 2019 | 122 | 2019 |
| Multi-view multi-label learning with sparse feature selection for image annotation Y Zhang, J Wu, Z Cai, SY Philip IEEE Transactions on Multimedia 22 (11), 2844-2857, 2020 | 97 | 2020 |
| Memetic extreme learning machine Y Zhang, J Wu, Z Cai, P Zhang, L Chen Pattern Recognition 58, 135-148, 2016 | 77 | 2016 |
| Spectral–spatial and superpixelwise PCA for unsupervised feature extraction of hyperspectral imagery X Zhang, X Jiang, J Jiang, Y Zhang, X Liu, Z Cai IEEE Transactions on Geoscience and Remote Sensing 60, 1-10, 2021 | 63 | 2021 |
| Robust dual graph self-representation for unsupervised hyperspectral band selection Y Zhang, X Wang, X Jiang, Y Zhou IEEE Transactions on Geoscience and Remote Sensing 60, 1-13, 2022 | 54 | 2022 |
| Instance cloned extreme learning machine Y Zhang, J Wu, C Zhou, Z Cai Pattern Recognition 68, 52-65, 2017 | 50 | 2017 |
| Hierarchical ensemble of extreme learning machine Y Cai, X Liu, Y Zhang, Z Cai Pattern Recognition Letters 116, 101-106, 2018 | 48 | 2018 |
| A multiobjective optimization-based sparse extreme learning machine algorithm Y Wu, Y Zhang, X Liu, Z Cai, Y Cai Neurocomputing 317, 88-100, 2018 | 36 | 2018 |
| Spectral–spatial feature extraction with dual graph autoencoder for hyperspectral image clustering Y Zhang, Y Wang, X Chen, X Jiang, Y Zhou IEEE Transactions on Circuits and Systems for Video Technology 32 (12), 8500 …, 2022 | 34 | 2022 |
| Spectral-spatial hyperspectral image classification with superpixel pattern and extreme learning machine Y Zhang, X Jiang, X Wang, Z Cai Remote Sensing 11 (17), 1983, 2019 | 33 | 2019 |
| Laplacian regularized spatial-aware collaborative graph for discriminant analysis of hyperspectral imagery X Jiang, X Song, Y Zhang, J Jiang, J Gao, Z Cai Remote Sensing 11 (1), 29, 2018 | 20 | 2018 |
| Latent representation learning based autoencoder for unsupervised feature selection in hyperspectral imagery X Wang, Z Wang, Y Zhang, X Jiang, Z Cai Multimedia Tools and Applications, 1-15, 2022 | 18 | 2022 |
| Unsupervised dimensionality reduction for hyperspectral imagery via laplacian regularized collaborative representation projection X Jiang, L Xiong, Q Yan, Y Zhang, X Liu, Z Cai IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2022 | 17 | 2022 |
| Multi-view fusion with extreme learning machine for clustering Y Zhang, J Wu, C Zhou, Z Cai, J Yang, PS Yu ACM Transactions on Intelligent Systems and Technology (TIST) 10 (5), 1-23, 2019 | 13 | 2019 |
| A memetic algorithm based extreme learning machine for classification Y Zhang, Z Cai, J Wu, X Wang, X Liu 2015 International Joint Conference on Neural Networks (IJCNN), 1-8, 2015 | 13 | 2015 |
| Deep mutual information subspace clustering network for hyperspectral images T Li, Y Cai, Y Zhang, Z Cai, X Liu IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2022 | 12 | 2022 |
| Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral Image Classification J Zhang, Y Zhang, Y Zhou Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 6 | 2023 |
| Tensor-based unsupervised multi-view feature selection for image recognition Y Zhang, X Wang, Z Cai, Y Zhou, SY Philip 2021 IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2021 | 6 | 2021 |
| Graph learning based autoencoder for hyperspectral band selection Y Zhang, X Wang, Z Wang, X Jiang, Y Zhou ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 5 | 2022 |
| Spectral-spatial superpixel anchor graph-based clustering for hyperspectral imagery X Chen, Y Zhang, X Feng, X Jiang, Z Cai IEEE Geoscience and Remote Sensing Letters, 2023 | 4 | 2023 |
