Sparse Unidirectional Domain Adaptation Algorithm for Instrumental Variation Correction of Electronic Nose Applied to Lung Cancer Detection

In this work, two electronic nose (E-nose) prototypes for lung cancer detection via breath analysis are built. The instrumental variation between them is a critical problem, as it impacts on the generalization performance of classification model. The conventional domain adaption (DA) methods are based on the assumption that the data quality of both devices is high enough. However, it is not true in our case because the concentrations of volatile organic compounds (VOCs) biomarkers in breath are very low and the acquisition process of breath samples is prone to many confounding factors. As a countermeasure, a novel and effective two-step sparse unidirectional domain adaptation (SUDA) algorithm is proposed. In the first step, the data quality of each domain is improved, at the meantime, the distribution discrepancy between source and target domain is reduced by finding the common discriminative features via the sparse group lasso algorithm. In the second step, the distribution gap is further narrowed down by adopting the strategies of distribution alignment, local geometric characteristics preserving and label dependence constraint. Experimental results show that the proposed method is not only significantly effective for correcting instrumental variation in the two homemade lung-cancer-detection E-noses, but also demonstrates superior performance on a public E-nose instrumental variation dataset.