Domain Adaptation Transfer Learning by SVM Subject to a Maximum-Mean-Discrepancy-like Constraint

Xiaoyi Chen, Régis Lengellé

2017

Abstract

This paper is a contribution to solving the domain adaptation problem where no labeled target data is available.A new SVM approach is proposed by imposing a zero-valued Maximum Mean Discrepancy-like constraint.This heuristic allows us to expect a good similarity between source and target data, after projection onto an efficient subspace of a Reproducing Kernel Hilbert Space. Accordingly, the classifier will perform well on source and target data. We show that this constraint does not modify the quadratic nature of the optimization problem encountered in classic SVM, so standard quadratic optimization tools can be used. Experimental results demonstrate the competitiveness and efficiency of our method.

Paper Citation

in Harvard Style

Chen X. and Lengellé R. (2017). Domain Adaptation Transfer Learning by SVM Subject to a Maximum-Mean-Discrepancy-like Constraint . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 89-95. DOI: 10.5220/0006119900890095

in Bibtex Style

@conference{icpram17,
author={Xiaoyi Chen and Régis Lengellé},
title={Domain Adaptation Transfer Learning by SVM Subject to a Maximum-Mean-Discrepancy-like Constraint},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={89-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006119900890095},
isbn={978-989-758-222-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Domain Adaptation Transfer Learning by SVM Subject to a Maximum-Mean-Discrepancy-like Constraint
SN - 978-989-758-222-6
AU - Chen X.
AU - Lengellé R.
PY - 2017
SP - 89
EP - 95
DO - 10.5220/0006119900890095