Feature extraction of electronic nose for classification of indoor pollution gases based on kernel entropy component analysis

Feature extraction is important for electronic nose (E-nose), when it is used to classify different gases or odours. A novel feature extraction technique of E-nose based on kernel entropy component analysis (KECA) is presented in this paper. KECA is integrated with Renyi entropy and extracts the features from the kernel Hilbert space by projecting the input dataset onto the kernel principal component analysis (KPCA) axes that preserve the most Renyi entropy. Besides KECA, independent component analysis and KPCA are also used to deal with the original feature matrix of four different indoor pollution gases acquired by E-nose. Experimental results prove that the classification accuracy of KECA is better than other considered techniques.