Name
Abstract | ||
|---|---|---|
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 different DA scenarios involving unsupervised adaptation of multivariate calibration models between different process lines in Melamine production and equality to SoA on two well-known benchmark datasets from analytical chemistry involving (unsupervised) model adaptation between different spectrometers. The former dataset is published with this work1 1https://github.com/RNL1/Melamine-Dataset. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.knosys.2020.106447 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Transfer learning,Domain adaptation,Moment alignment,Chemometrics,Calibration model adaptation,Partial least squares | Journal | 210 |
ISSN | Citations | PageRank |
0950-7051 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramin Nikzad-Langerodi | 1 | 0 | 0.34 |
| Werner Zellinger | 2 | 32 | 4.27 |
| Susanne Saminger-Platz | 3 | 76 | 10.94 |
| Bernhard Alois Moser | 4 | 0 | 0.34 |