Domain-Invariant Regression under Beer-Lambert?s Law
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
International Conference on Machine Learning and Applications
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
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.
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