Discriminative dimensionality reduction for sensor drift compensation in electronic nose: a robust, low-rank, and sparse representation method

Abstract Sensor drift, which is a critical issue in the field of sensor measurements, has plagued the sensor community in the past several decades. How to tackle the sensor drift problem using expert and intelligent systems has gained increasing attention. Most sensor drift compensation methods ignore the sparse and low-rank characteristics of sensor signals. In this paper, we propose a discriminative dimensionality reduction method for sensor drift compensation in the electronic nose. The proposed method consists of four major components. 1) The distribution discrepancy between source data and target data is alleviated via projecting all the data into a common subspace. 2) A sparse and low-rank reconstruction coefficient matrix is employed to preserve the global and local structures of sensor signals. 3) An error matrix is introduced to deal with outliers. 4) The source data label information is taken into consideration to avoid overlapping of samples with different labels in the common subspace. The formulated minimization problem with constraints can be solved in an iterative manner. The effectiveness of the proposed method has been verified by conducting experiments on two sensor drift datasets. The proposed method may provide new insights into the gas sensor drift compensation systems or other expert and intelligent systems.