FEDA: A Nonlinear Subspace Projection Approach for Electronic Nose Data Classification
Xi Chen, Yi Lin, Ran Liu
Abstract
The electronic nose (e-nose) is susceptible to sensor drift and instrumental variation, which may result in distribution discrepancy in data collected, hence leading to classification performance degradation. It is necessary to apply domain adaptation to solve this problem. A nonlinear subspace projection approach named feature entropy domain adaption (FEDA) is proposed for domain adaptation for e-nose data classification. One important aspect of FEDA is that adversarial training is introduced to minimize the distribution discrepancy between source and target domains. No projection matrix and parameter fine-tuning are needed anymore in comparison with the popular linear subspace projection approaches. In addition, feature norm and conditional entropy are introduced into adversarial training in FEDA to reduce the decision boundary uncertainty and the overlap between classes, respectively. Experimental results show that the FEDA can deal with the distribution discrepancy of e-nose effectively, and can achieve satisfactory classification accuracy on various datasets. Source code can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/threedteam/DA_FEDA</uri> .