Abstract
When an electronic nose (E-nose) is used to predict the classes of wound infection, its result is not ideal if the original feature matrix extracted from the response of sensors is put into the classifier directly. To acquire more useful information which can improve E-nose's classification accuracy, we present a novel weighted kernel principal component analysis (KPCA) method to process this matrix. In addition, we have also compared it with other existing methods including independent component analysis (ICA), orthogonal signal correction (OSC), locality preserving projections (LPP), principal component analysis (PCA), KPCA and the traditional weighted KPCA. The odors of four different classes of wounds (uninfected and infected with Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa) are used as the original response of E-nose. Experimental results have demonstrated that the proposed weighted KPCA method outperforms other feature extraction methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.