  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.
