Domain adaptive partial least squares regression

In practical applications, the problem of training- and test-samples from different distributions is often encountered, such as instruments or external environmental factors change when measuring the data. Therefore, a multivariate calibration model established, based on the training set needs to be adaptive to meet the requirements of test samples from different domains. Extracting domain adaptive latent variables is an effective method to address such issues, but related studies about making multivariate calibration models adaptive to different domains in unsupervised methods are rarely reported. In this paper, a domain adaptive partial least squares regression is proposed, which uses the Hilbert-Schmidt independence criterion to evaluate the independence of the extracted latent variables and domain labels. In both the original space and reproducing kernel Hilbert space, the proposed method can obtain a closed form of the domain adaptive projection vector, which ensures the high efficiency of the model calculation. The validity of the proposed method is verified by several simulation data sets and real near-infrared spectral data sets. The experimental results show that the proposed method has good universally for unsupervised regression calibration transfer.