The Efficiency of Feature Feedback Using R-LDA with Application to Portable E-Nose System

Abstract

AbstractIn this paper, we improve the performance of Feature Feedback and present its application for vapor classification in a portable E-Nose system. Feature Feedback is a preprocessing method which detects and removes unimportant information from input data so that classification performance is improved. In our original Feature Feedback algorithm, PCA is used before LDA in order to avoid the small sample size (SSS) problem but it is said that this may cause loss of significant discriminant information for classification. To overcome this, in the proposed method, we improve Feature Feedback using regularized Fisher’s separability criterion to extract the features and apply it to E-Nose system. The experimental result shows that the proposed method works well.Keywordse-nose systemvapor classificationfeature feedbackdiscriminant feature