期刊论文

Mou, Yi;Zhou, Long

英文

Optik

0030-4026

2018

157

343-352

10.1016/j.ijleo.2017.11.078

This work is supported by Chutian Scholar Programm – Chutian Student of Hubei Province , Hubei Provincial Department of Education (No. D20161705 ), Science and Technology Department of Hubei Province (No. 2016CFB298 ), Grain Administration of Hubei Province (No. 2060404 ), the Recruitment Program of Wuhan Polytechnic University (No. 2017RZ05 ), Research and Innovation Initiatives of WHPU (No. 2017y27 and 2016J06 ).

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Feature extraction is a key problem in spectral analysis. Spectrum collected with spectrometer have latent low-rank component. If spectrum can be represented as a superposition of low-rank component and an approximation term, the spectrum feature is obtained. In this paper, a novel low-rank based infrared spectral feature extraction method is proposed. Employing a slide window to convert a single spectrum into a matrix, which can be decomposed as the superposition of a low-rank component and feature. In machine learning, nuclear norm is employe...