A novel sensor feature extraction based on kernel entropy component analysis for discrimination of indoor air contaminants

• This paper proposes to extract principal component feature using a kernel entropy component analysis technique.
• A KECA plus SVM algorithm is proposed for recognition of multiple indoor air contaminants.
• Experiments on discrimination of six kinds of indoor air contaminants demonstrate the effectiveness of KECA for feature extraction.

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