Domain-invariant regression under Beer-Lambert’s Law

R. Nikzad-Langerodi, B. Moser, W. Zellinger, S. Saminger-Platz. Domain-invariant regression under Beer-Lambert’s Law. pages 581-856, DOI, 2, 2020.

  • Ramin Nikzad-Langerodi
  • Bernhard A. Moser
  • Werner Zellinger
  • Susanne Saminger-Platz
  • M. Arif Wani
  • Taghi M. Khoshgoftaar
  • Dingding Wang
  • Huanjing Wang
  • Naeem (Jim) Seliya
BuchProceedings of the 18th IEEE International Conference of Machine Learning and Applications (ICMLA 2019)
TypIn Konferenzband

We consider the problem of unsupervised domain adaptation (DA) in regression under the assumption of linear hypotheses (e.g. Beer-Lambert's law) - a task recurrently encountered in analytical chemistry. Following the ideas from the non-linear iterative partial least squares (NIPALS) method, we propose a novel algorithm that identifies a low-dimensional subspace aiming at the following two objectives: i) the projections of the source domain samples are informative w.r.t. the output variable and ii) the projected domain-specific input samples have a small covariance difference. In particular, the latent variable vectors that span this subspace are derived in closed-form by solving a constrained optimization problem for each subspace dimension adding flexibility for balancing the two objectives. We demonstrate the superiority of our approach over several state-of-the-art (SoA) methods on two typical DA scenarios involving unsupervised adaptation of multivariate calibration models between different process lines in melamine production and equality to SoA on a well-known benchmark dataset from analytical chemistry involving (unsupervised) model adaptation between different spectrometers. The former data set is provided along with this paper.