Fei Zhu – Papers

Center for Applied Mathematics, Tianjin University

ReaserchGate

Journal Paper

1. Q. Yun, F. Zhu*, Bo Xi, and Lifu Song. Robust Multi-read Reconstruction from Noisy Clusters Using Deep Neural Network for DNA Storage,Computational and Structural Biotechnology Journal, 23(2024):1076-1087. (IF:4.5) [link]
2.C. Dou, Y.Yang, F. Zhu, B. Li*, Y. Duan*, Explorer: efficient DNA coding by De Bruijn graph toward arbitrary local and global biochemical constraints, Briefings in Bioinformatics, Volume 25, Issue 5, September 2024. (IF:6.8) [link]

1. J. Gao, J. Shi and F. Zhu*, Robust Sparse Unmixing via Continuous Mixed Norm to Address Mixed Noise, IEEE Geoscience and Remote Sensing Letters., doi: 10.1109/LGRS.2025.3548697, 2025. (IF:4.0) [link]
2. T. Fang, F. Zhu* and J. Chen. Hyperspectral Unmixing Based on Multilinear Mixing Model Using Convolutional Autoencoders. IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-16, 2024.(IF:7.5) [link]
3. A.J.X. Guo and F. Zhu*. Improving deep hyperspectral image classification performance with spectral unmixing, Signal Processing,vol. 183, 2021.(IF:4.6)[link]
4. Y. Zhang, F. Zhu*, A Kernel-based Weight Decorrelation for Regularizing CNNs, Neurocomputing,vol. 429, pp.47-59, 2021. (IF:5.7)[link]
5. X. Zhao, Y. Liang, A.J.X. Guo*, F. Zhu, Classification of small-scale hyperspectral images with multi-source deep transfer learning, Remote Sensing Letters, vol. 11, no. 4, pp. 303-312, 2020.(IF:2.02) [link]
6. M. Li, F. Zhu*, A.J.X. Guo and J. Chen, A Graph Regularized Multilinear Mixing Model for Nonlinear Hyperspectral Unmixing, Remote Sensing, 11(19),2188, 2019.(IF:4.11) [link]
7. Y. Liang, X. Zhao, A.J.X. Guo* and F. Zhu. Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field, IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 6, pp. 1042-1046, June 2020.(IF:3.53)
8. A.J.X. Guo and F. Zhu*. A CNN-based Spatial Feature Fusion Algorithm for Hyperspectral Imagery Classification,IEEE Transactions on Geoscience and Remote Sensing,57(9), pp.7170-7181, Sept.2019.(IF:5.63)[link]
9. A.J.X. Guo and F. Zhu*. Spectral-Spatial Feature Extraction and Classification by ANN Supervised With Center Loss in Hyperspectral Imagery,IEEE Transactions on Geoscience and Remote Sensing,57(3),pp.1755-1767, Mar.2019.(IF:5.63) [link]
10. F. Zhu, A. Halimi, P. Honeine*, B. Chen, and N. Zheng. Correntropy Maximization via ADMM: Application to Robust Hyperspectral Unmixing,IEEE Transactions on Geoscience and Remote Sensing,55(9),pp.4944-4955, Sept.2017.(IF:4.66) [link]
11. F. Zhu and P. Honeine*. Online kernel nonnegative matrix factorization, Signal Processing,131,143-153, Feb. 2017.(IF:3.47) [link]
12. F. Zhu and P. Honeine*. Bi-objective nonnegative matrix factorization Linear Versus Kernel-Based Models,IEEE Transactions on Geoscience and Remote Sensing,54(7),pp.4012-4022, Apr.2016.(IF:4.94) [link]

Conference Paper

1. M. Li, F. Zhu* and A.J.X. Guo, A robust multilinear mixing model with L2,1 norm for unmixing hyperspectral images, IEEE International Conference on Visual Communications and Image Processing (VCIP):2020.
2. F. Zhu, P. Honeine, J. Chen Pixel-wise linear/nonlinear nonnegative matrix factorization for unmixing of hyperspectral data. 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): 4--8 May, 2020. [link]
3. F. Zhu, A. Halimi, P. Honeine, B. Chen, N. Zheng. ADMM for Maximum Correntropy Criterion. 2016 International Joint Conference on Neural Network, Vancouver, Canada, (IJCNN): 24--29 July, 2016. [link]
4. F. Zhu and P. Honeine. Pareto front of bi-objective kernel-based nonnegative matrix factorization. 23th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). Bruges, Belgium, 22--24 Apr. 2015.
5. F. Zhu, P. Honeine and K. Maya. Kernel non-negative matrix factorization without the pre-image problem. 24th IEEE workshop on Machine Learning for Signal Processing (MLSP). Reims, France,21--24 Sept. 2014. [link]

Fei Zhu – Papers

Journal Paper

Conference Paper

Patent