Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/44028
Title: A novel sensor feature extraction based on kernel entropy component analysis for discrimination of indoor air contaminants
Authors: Peng, X
Zhang, L
Tian, F
Zhang, D 
Keywords: Classification
Component analysis
Electronic nose
Feature extraction
Renyi entropy
Issue Date: 2015
Publisher: Elsevier
Source: Sensors and actuators. A, Physical, 2015, v. 234, p. 143-149 How to cite?
Journal: Sensors and actuators. A, Physical 
Abstract: Component analysis techniques for feature extraction in multi-sensor system (electronic nose) have been studied in this paper. A novel nonlinear kernel based Renyi entropy component analysis method is presented to address the feature extraction problem in sensor array and improve the odor recognition performance of E-nose. Specifically, a kernel entropy component analysis (KECA) as a nonlinear dimension reduction technique based on the Renyi entropy criterion is presented in this paper. In terms of the popular support vector machine (SVM) learning technique, a joint KECA-SVM framework is proposed as a system for nonlinear feature extraction and multi-class gases recognition in E-nose community. In particular, the comparisons with PCA, KPCA and ICA based component analysis methods that select the principal components with respect to the largest eigen-values or correlation have been fully explored. Experimental results on formaldehyde, benzene, toluene, carbon monoxide, ammonia and nitrogen dioxide demonstrate that the KECA-SVM method outperforms other methods in classification performance of E-nose. The MATLAB implementation of this work is available online at http://www.escience.cn/people/lei/index.html.
URI: http://hdl.handle.net/10397/44028
ISSN: 0924-4247
EISSN: 1873-3069
DOI: 10.1016/j.sna.2015.09.009
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