Sensor Drift Compensation of E-Nose Systems With Discriminative Domain Reconstruction Based on an Extreme Learning Machine

Abstract

Electronic noses (E-noses) have been successfully applied in various fields. However, as a result of the inherent variability of chemical sensors, a signal processing algorithm well trained with the data from the existing domain often cannot be directly applied to the domain of interest. This severely limits the large-scale use of E-noses. In this paper, an unsupervised discriminative domain reconstruction based extreme learning machine (DDR-ELM) is proposed to compensate for such drifts/shifts and address the domain adaptation problem. Specifically, the method learns a domain-invariant space to minimize the distribution difference between different domains by discriminatively handling the different domain data using an extreme learning machine (ELM) framework. This method retains as many of the useful spatial characteristics of the source domain as possible and reduces the divergence between domains without any labeled target domain data. It avoids the cost and labor of obtaining access to the labels of data from the domain of interest. In addition, both the domain reconstruction and classification processes utilize the ELM, which is solved by pseudoinverse operations without error back-propagation iterations, consequently keeping computational complexity low. Experiments on different sensor datasets demonstrate that the proposed method is superior to several state-of-the-art drift/shift compensation methods not only in classification accuracy but also maintaining higher efficiency.