DMCA
Co-regularization Based Semi-supervised Domain Adaptation
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BibTeX
@MISC{Iii_co-regularizationbased,
author = {Hal Daumé Iii and Abhishek Kumar and Avishek Saha},
title = {Co-regularization Based Semi-supervised Domain Adaptation},
year = {}
}
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Abstract
This paper presents a co-regularization based approach to semi-supervised domain adaptation. Our proposed approach (EA++) builds on the notion of augmented space (introduced in EASYADAPT (EA) [1]) and harnesses unlabeled data in target domain to further assist the transfer of information from source to target. This semi-supervised approach to domain adaptation is extremely simple to implement and can be applied as a pre-processing step to any supervised learner. Our theoretical analysis (in terms of Rademacher complexity) of EA and EA++ show that the hypothesis class of EA++ has lower complexity (compared to EA) and hence results in tighter generalization bounds. Experimental results on sentiment analysis tasks reinforce our theoretical findings and demonstrate the efficacy of the proposed method when compared to EA as well as few other representative baseline approaches. 1
Keyphrases
semi-supervised domain adaptation hence result representative baseline approach unlabeled data theoretical finding target domain rademacher complexity semi-supervised approach theoretical analysis sentiment analysis task experimental result supervised learner generalization bound hypothesis class pre-processing step augmented space
