Jiangtao Peng

Professor

Faculty of Mathematics and Statistics, Hubei University

368 Youyi Avenue, Wuchang District, Wuhan 430062, P. R. China

CONTACT

pengjt1982@126.com

RESEARCH INTERESTS

• Pattern Recognition and Machine Learning

• Hyperspectral Image Processing

• Spectral Signal Analysis

RESEARCH EXPERIENCE

• 2015.07~now.Associate Professor, Hubei University.

• 2017.09~2018.09.Visiting Scholar, Mississippi State University.

• 2015.06~2015.09.Research Fellow, University of Macau.

• 2013.08~2014.07.Research Fellow, University of Macau.

• 2012.09~2014.10.Postdoctoral Researcher, Hubei University.

EDUCATIONAL BACKGROUND

• 2008.09~2011.06.Ph.D. Institute of Automation, Chinese Academy of Sciences.

• 2005.09~2008.07.M.Sci. Applied Mathematics, Hubei University.

• 2001.09~2005.07.B.Sci. Information and Computing Science, Hubei University.

PUBLICATIONS

[1]Jiangtao Peng*, Long Tian, “Robust ridge regression based on self-paced learning for multivariate calibration,” Chemometrics and Intelligent Laboratory Systems, vol. 176, pp. 44-52, 2018.

[2] Lu Guo,Jiangtao Peng, Qiwei Xie, “Maximum likelihood estimation based regression for multivariate calibration,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 189, pp. 316–321, 2018.

[3]Jiangtao Peng*, Qian Du, “Robust Joint Sparse Representation based on maximum correntropy criterion for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 12, pp. 7152–7164, 2017.

[4]Jiangtao Peng*, Lu Guo, et al., “Maximum correntropy criterion based regression for multivariate calibration,” Chemometrics and Intelligent Laboratory Systems, vol. 161, pp. 27-33, 2017.

[5]Jiangtao Peng*, Hong Chen, Yicong Zhou, Luoqing Li, “Ideal regularized composite kernel for hyperspectral image classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 4, pp. 1563-1574, 2017.

[6] Xiang Chen, Shuying Li,Jiangtao Peng*, “Hyperspectral imagery classification with multiple regularized collaborative representations,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 7, pp. 1121–1125, 2017.

[7]Jiangtao Peng*, Lefei Zhang, Luoqing Li, “Regularized set-to-set distance metric learning for hyperspectral image classification,” Pattern Recognition Letters, vol. 83, pp. 143–151, 2016.

[8] Chen Chen, Na Chen*,Jiangtao Peng, “Nearest Regularized Joint Sparse Representation for Hyperspectral Image Classification,” IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 3, pp. 424–428, 2016.

[9]Jiangtao Peng, Yicong Zhou*, C.L. Philip Chen, “Region-kernel-based support vector machines for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 9, pp. 4810–4824, 2015.

[10] Yicong Zhou,Jiangtao Peng*, C.L. Philip Chen, “Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 2, pp. 1082–1095, 2015.

[11] Yicong Zhou,Jiangtao Peng*, C.L. Philip Chen, “Extreme learning machine with composite kernels for hyperspectral image classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 6, pp. 2351–2360, 2015.