Subspace alignment based on an extreme learning machine for electronic nose drift compensation

Abstract

The drift caused by gas sensors has always been a bottleneck in the development of electronic nose (E-nose) systems. Traditional drift compensation methods directly correct the drift components, making such approaches time-consuming and laborious. In the field of E-nose drift compensation, cross-domain adaption learning is an efficient technique. In this paper, we propose a novel subspace alignment extreme learning machine (SAELM) that considers multiple criteria to construct a unified extreme learning machine (ELM)-based feature representation space and thus achieve domain alignment. First, the method minimizes both the geometric and statistical distributions between different domains. Second, the dependence between features and labels is enhanced using the Hilbert–Schmidt independence criterion (HSIC) to alleviate the blurring of the correspondence between the two caused by drift. Third, to improve the feature extraction ability of the subspace learning method, the l2,1 norm is leveraged to constrain the output weights of the ELM. The aim of this method is to learn a robust subspace to increase the consistency between domains and enhance the feature–label​ dependency of the source domain while preserving the intrinsic information of both domains. Extensive experiments on sensor drift data are conducted, and the proposed SAELM method yields the greatest improvements on E-nose drift datasets.