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

Xiongwei Peng, Lei Zhang, Fengchun Tian, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)143-149
Number of pages7
JournalSensors and Actuators, A: Physical
Volume234
DOIs
Publication statusPublished - 1 Oct 2015

Keywords

  • Classification
  • Component analysis
  • Electronic nose
  • Feature extraction
  • Renyi entropy

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Condensed Matter Physics
  • Surfaces, Coatings and Films
  • Metals and Alloys
  • Electrical and Electronic Engineering

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