Abstract
Sensor drift is a well-known issue in the field of sensors and measurement and has plagued the sensor community for many years. In this paper, we propose a sensor drift correction method to deal with the sensor drift problem. Specifically, we propose a discriminative subspace projection approach for sensor drift reduction in electronic noses. The proposed method inherits the merits of the subspace projection method called domain regularized component analysis. Moreover, the proposed method takes the source data label information into consideration, which minimizes the within-class variance of the projected source samples and at the same time maximizes the between-class variance. The label information is exploited to avoid overlapping of samples with different labels in the subspace. Experiments on two sensor drift datasets have shown the effectiveness of the proposed approach.
Highlights
Electronic noses (E-noses) have been widely used in a wealth of domains, for instance, indoor and outdoor air quality monitoring [1], [2], [44], medical diagnosis [3], [4], detection of polluting gases from vehicles [5], [6], and fruit quality control [7]
We focus on developing machine learning approaches for sensor drift compensation
The exploitation of label information can avoid overlapping of the samples with different labels in the subspace
Summary
Electronic noses (E-noses) have been widely used in a wealth of domains, for instance, indoor and outdoor air quality monitoring [1], [2], [44], medical diagnosis [3], [4], detection of polluting gases from vehicles [5], [6], and fruit quality control [7]. Li: Anti-Drift in Electronic Nose via Dimensionality Reduction: Discriminative Subspace Projection Approach the response of each sensor independently, which are simple but extremely sensitive to sample rate variations [17], [18]. The main contribution of this paper is to propose a discriminative subspace projection method for drift reduction in electronic noses. The proposed approach exploits the class label information of the source data in order to avoid the overlapping of samples with different labels in the subspace. 3) We have shown that the effectiveness of the proposed approach with comparisons with the state-of-the-art methods on two public gas sensor drift datasets.
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