Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach

TL;DR

This paper introduces a discriminative subspace projection method to effectively reduce sensor drift in electronic noses by leveraging label information to enhance class separation.

Contribution

It proposes a novel sensor drift correction technique that incorporates label information into subspace projection, improving drift mitigation in electronic nose sensors.

Findings

Effective reduction of sensor drift demonstrated on two datasets.

Outperforms existing drift correction methods.

Enhances class separation in sensor data.

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.