Semi-supervised single- And multi-domain regression with multi-domain training

Published

Journal Article

© The authors 2012. We address the problems of multi- and single-domain regression based on distinct and unpaired labeled training sets for each of the domains and a large unlabeled training set from all domains. We formulate these problems as a Bayesian estimation with partial knowledge of statistical relations. We propose a worst-case design strategy and study the resulting estimators. Our analysis explicitly accounts for the cardinality of the labeled sets and includes the special cases in which one of the labeled sets is very large or, in the other extreme, completely missing. We demonstrate our estimators in the context of removing expressions from facial images and in the context of audio-visual word recognition, and provide comparisons to several recently proposed multi-modal learning algorithms.

Full Text

Duke Authors

Cited Authors

  • Michaeli, T; Eldar, YC; Sapiro, G

Published Date

  • January 1, 2012

Published In

Volume / Issue

  • 1 / 1

Start / End Page

  • 68 - 97

Electronic International Standard Serial Number (EISSN)

  • 2049-8772

Digital Object Identifier (DOI)

  • 10.1093/imaiai/ias003

Citation Source

  • Scopus