LAUSR

Sensor drift is a well-known issue in the field of sensors and measurement. It 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. In contrast to domain regularized component analysis, the proposed method makes use of the class label of data in the source domain. Specifically, we propose a discriminative subspace projection approach for sensor drift reduction in electronic noses. The proposed method has multiple properties. (1) It inherits the merits of the subspace projection approach called domain regularized component analysis via introducing a regularization parameter to tackle the sample size imbalance problem. (2) 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. An efficient method based on generalized eigenvalue decomposition is employed to solve the optimization problem. Experiments on two sensor drift datasets have shown the effectiveness of the proposed approach.