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Sensors 2016, 16(7), 1019; doi:10.3390/s16071019

A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections

College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
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Author to whom correspondence should be addressed.
Academic Editor: Carmen Horrillo Güemes
Received: 12 April 2016 / Revised: 12 June 2016 / Accepted: 20 June 2016 / Published: 30 June 2016
(This article belongs to the Special Issue E-noses: Sensors and Applications)
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Abstract

An electronic nose (E-nose) consisting of 14 metal oxide gas sensors and one electronic chemical gas sensor has been constructed to identify four different classes of wound infection. However, the classification results of the E-nose are not ideal if the original feature matrix containing the maximum steady-state response value of sensors is processed by the classifier directly, so a novel pre-processing technique based on supervised locality preserving projections (SLPP) is proposed in this paper to process the original feature matrix before it is put into the classifier to improve the performance of the E-nose. SLPP is good at finding and keeping the nonlinear structure of data; furthermore, it can provide an explicit mapping expression which is unreachable by the traditional manifold learning methods. Additionally, some effective optimization methods are found by us to optimize the parameters of SLPP and the classifier. Experimental results prove that the classification accuracy of support vector machine (SVM combined with the data pre-processed by SLPP outperforms other considered methods. All results make it clear that SLPP has a better performance in processing the original feature matrix of the E-nose. View Full-Text
Keywords: sensor data; electronic nose; SLPP; data pre-processing; wound infection sensor data; electronic nose; SLPP; data pre-processing; wound infection
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Jia, P.; Huang, T.; Wang, L.; Duan, S.; Yan, J.; Wang, L. A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections. Sensors 2016, 16, 1019.

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