On Regularization Parameter Estimation under Covariate Shift

Conference paper (2016)

Authors

W.M. Kouw Pattern Recognition and Bioinformatics -

M. Loog Pattern Recognition and Bioinformatics - , University of Copenhagen

Research Group

Pattern Recognition and Bioinformatics () (TU Delft)

Parameter estimation Estimation Pattern recognition Training Risk management Temperature measurement Training data

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Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

This paper identifies a problem with the usual procedure for L2-regularization parameter estimation in a domain adaptation setting. In such a setting, there are differences between the distributions generating the training data (source domain) and the test data (target domain). The usual cross-validation
procedure requires validation data, which can not be obtained from the unlabeled target data. The problem is that if one decides to use source validation data, the regularization parameter is underestimated. One possible solution is to scale the source validation data through importance weighting, but we show that
this correction is not sufficient. We conclude the paper with an empirical analysis of the effect of several importance weight estimators on the estimation of the regularization parameter.