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Title: Calibration transfer and drift compensation of e-noses via coupled task learning
Authors: Yan, K
Zhang, D 
Keywords: Calibration transfer
Electronic nose
Multi-task learning
Transfer learning
Transfer sample
Issue Date: 2016
Publisher: Elsevier
Source: Sensors and actuators. B, Chemical, 2016, v. 225, p. 288-297 How to cite?
Journal: Sensors and actuators. B, Chemical 
Abstract: The problems of instrumental variation and sensor drift are receiving increasing concerns in the field of electronic noses. Because the two problems can be uniformly viewed as a variation of the data distribution in the feature space, they can be handled by algorithms such as transfer learning. In this paper, we propose a novel algorithm framework called transfer sample-based coupled task learning (TCTL). It is based on transfer learning and multi-task learning. Given labeled samples in the source domain (i.e. from the master device or without drift) and a small set of transfer samples as inputs, TCTL simultaneously learns a prediction model for data in the source domain and one for data in the target domain (i.e. from the slave device or with drift). The transfer samples are incorporated into a regularization term of the objective function. TCTL is an extensible framework that can apply to various classification and regression models. When combined with the standardization error-based model improvement (SEMI) strategy, its accuracy can be further enhanced. Experiments on a multi-device dataset and a popular long-term drift dataset show that the proposed algorithms achieve better accuracy compared with typical existing methods with much fewer auxiliary samples needed, which proves their efficacy and usability in real-life applications.
ISSN: 0925-4005
EISSN: 1873-3077
DOI: 10.1016/j.snb.2015.11.058
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