期刊
KNOWLEDGE-BASED SYSTEMS
卷 210, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2020.106447
关键词
Transfer learning; Domain adaptation; Moment alignment; Chemometrics; Calibration model adaptation; Partial least squares
资金
- BMVIT
- BMDW
- Federal Province of Upper Austria
- Federal Province of Vienna
- Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology
- Province of Upper Austria
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 work(1) (C) 2020 Elsevier B.V. All rights reserved.
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