Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach
This addresses a long-standing sensor drift problem for the sensor and measurement community, but it is incremental as it builds on existing subspace projection methods.
The paper tackles sensor drift in electronic noses by proposing a discriminative subspace projection method that minimizes within-class variance and maximizes between-class variance using label information, showing effectiveness in experiments on two datasets.
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.