A novel orthogonal constraint-based subspace learning method for electronic nose drift compensation

Abstract

For the sensor drift problem in electronic nose systems, we propose a novel orthogonal constraint-based subspace learning technique in this study. First, the method minimizes the central distance between two domains and preserves the geometric structure of data in the subspace after projection. Second, a linear regression function based on the l2,1 norm is used to represent the mapping relationship between the subspace and the label space, allowing the correlation relationship between data before and after projection to be maintained, feature extraction ability to be improved, and noise robustness to be achieved. Third, the orthogonal constraint is utilized to encourage geometric interpretation and data reconstruction. Finally, we conduct experiments on typical sensor drift datasets with long-term drift, and the results demonstrate the effectiveness of the method.