期刊
PATTERN RECOGNITION LETTERS
卷 71, 期 -, 页码 59-65出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.patrec.2015.12.007
关键词
Spectral envelope; Infrared spectroscopy; dimensionality reduction
资金
- PICT PRH, ANPCyT, Argentina [0253 (2011), 2513 (2012)]
Infrared spectroscopy data is characterized by the presence of a huge number of variables. Applications of infrared spectroscopy in the mid-infrared (MIR) and near-infrared (NIR) bands are of widespread use in many fields. To effectively handle this type of data, suitable dimensionality reduction methods are required. In this paper, a dimensionality reduction method designed to enable effective Support Vector Machine Recursive Feature Elimination (SVM-RFE) on NIR/MIR datasets is presented. The method exploits the information content at peaks of the spectral envelope functions which characterize NIR/MIR spectra datasets. Experimental evaluation across different NIR/MIR application domains shows that the proposed method is useful for the induction of compact and accurate SVM classifiers for qualitative NIR/MIR applications involving stringent interpretability or time processing requirements. (C) 2015 Elsevier B.V. All rights reserved.
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