| Adjusting the imbalance ratio by the dimensionality of imbalanced data R Zhu, Y Guo, JH Xue Pattern Recognition Letters 133, 217-223, 2020 | 74 | 2020 |
| Spectral nonlocal restoration of hyperspectral images with low-rank property R Zhu, M Dong, JH Xue IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2014 | 54 | 2014 |
| LRID: A new metric of multi-class imbalance degree based on likelihood-ratio test R Zhu, Z Wang, Z Ma, G Wang, JH Xue Pattern Recognition Letters 116, 36-42, 2018 | 33 | 2018 |
| Matched shrunken cone detector (MSCD): Bayesian derivations and case studies for hyperspectral target detection Z Wang, R Zhu, K Fukui, JH Xue IEEE transactions on image processing 26 (11), 5447-5461, 2017 | 22 | 2017 |
| MvSSIM: A quality assessment index for hyperspectral images R Zhu, F Zhou, JH Xue Neurocomputing 272, 250-257, 2018 | 19 | 2018 |
| Cone-based joint sparse modelling for hyperspectral image classification Z Wang, R Zhu, K Fukui, JH Xue Signal Processing 144, 417-429, 2018 | 17 | 2018 |
| On hypothesis testing for comparing image quality assessment metrics [tips & tricks] R Zhu, F Zhou, W Yang, JH Xue IEEE Signal Processing Magazine 35 (4), 133-136, 2018 | 14 | 2018 |
| Constrained mutual convex cone method for image set based recognition N Sogi, R Zhu, JH Xue, K Fukui Pattern Recognition 121, 108190, 2022 | 11 | 2022 |
| On the orthogonal distance to class subspaces for high-dimensional data classification R Zhu, JH Xue Information Sciences 417, 262-273, 2017 | 11 | 2017 |
| Building a discriminatively ordered subspace on the generating matrix to classify high-dimensional spectral data R Zhu, K Fukui, JH Xue Information Sciences 382, 1-14, 2017 | 11 | 2017 |
| Clustering driving styles via image processing R Zhu, MV Wüthrich Annals of Actuarial Science 15 (2), 276-290, 2021 | 10 | 2021 |
| ReNAP: Relation network with adaptiveprototypical learning for few-shot classification X Li, Y Li, Y Zheng, R Zhu, Z Ma, JH Xue, J Cao Neurocomputing 520, 356-364, 2023 | 8 | 2023 |
| Learning distance to subspace for the nearest subspace methods in high-dimensional data classification R Zhu, M Dong, JH Xue Information Sciences 481, 69-80, 2019 | 7 | 2019 |
| Locally-Enriched Cross-Reconstruction for Few-Shot Fine-Grained Image Classification X Li, Q Song, J Wu, R Zhu, Z Ma, JH Xue IEEE Transactions on Circuits and Systems for Video Technology, 2023 | 5 | 2023 |
| Robust classification via support vector machines AV Asimit, I Kyriakou, S Santoni, S Scognamiglio, R Zhu Risks 10 (8), 154, 2022 | 4 | 2022 |
| Lightweight Single Image Super-Resolution With Similar Feature Fusion Block Z Wang, Y Liu, R Zhu, W Yang, Q Liao IEEE Access 10, 30974-30981, 2022 | 3 | 2022 |
| A novel separating hyperplane classification framework to unify nearest-class-model methods for high-dimensional data R Zhu, Z Wang, N Sogi, K Fukui, JH Xue IEEE transactions on neural networks and learning systems 31 (10), 3866-3876, 2019 | 3 | 2019 |
| Statistical hypothesis testing as a novel perspective of pooling for image quality assessment R Zhu, F Zhou, W Yang, JH Xue Signal Processing: Image Communication 114, 116942, 2023 | 1 | 2023 |
| Generalisations of stochastic supervision models X Lu, Y Qiao, R Zhu, G Wang, Z Ma, JH Xue Pattern Recognition 109, 107575, 2021 | 1 | 2021 |
| Selecting bivariate copula models using image recognition A Tsanakas, R Zhu ASTIN Bulletin: The Journal of the IAA 52 (3), 707-734, 2022 | | 2022 |
