On regularization parameter estimation under covariate shift (2016-07-31T00:00:00.000000Z)

TL;DR

This paper identifies a problem with the usual procedure for L2-regularization parameter estimation in a domain adaptation setting and concludes with an empirical analysis of the effect of several importance weight estimators on the estimation of the regularization parameter.

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.

Authors

Wouter M. Kouw

2 papers

M. Loog

2 papers

On regularization parameter estimation under covariate shift

TL;DR

Abstract

Authors

References24 items

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UCI machine learning repository

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