Boris Chidlovskii, Gabriela Csurka, Stéphane Clinchant
CLEF, Toulouse, France, 8-11 September, 2015
In this paper we address the problem of domain adaptation using
multiple source domains. We extend the XRCE contribution to Clef’14 Domain
Adaptation challenge [7] with the new methods and new datasets. We describe a
new class of domain adaptation technique based on stacked marginalized denoising
autoencoders (sMDA). It aims at extracting and denoising features common
to both source and target domains in the unsupervised mode. Noise marginalization
allows to obtain a closed form solution and to considerably reduce the
training time. We build a classification system which compares sMDA combined
with SVM or with Domain Specific Class Mean classifiers to the state-of-the art
in both unsupervised and semi-supervised settings. We report the evaluation results
for a number of image and text datasets.
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