Name
Abstract | ||
|---|---|---|
We consider the problem of unsupervised domain adaptation (DA) in regression under the assumption of linear hypotheses (e.g. Beer-Lambertu0027s 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICMLA.2019.00108 | ICMLA |
Field | DocType | Citations |
Regression,Subspace topology,Computer science,Partial least squares regression,Transfer of learning,Latent variable,Invariant (mathematics),Chemometrics,Law,Covariance | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramin Nikzad-Langerodi | 1 | 0 | 0.34 |
| Werner Zellinger | 2 | 0 | 0.34 |
| Susanne Saminger-Platz | 3 | 76 | 10.94 |
| Bernhard Moser | 4 | 0 | 0.34 |