DiReT: An effective discriminative dimensionality reduction approach for multi-source transfer learning

Document Type: Article

Authors

1 Faculty of IT & Computer Engineering, Urmia University of Technology, Urmia, Iran

2 School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.

Abstract

Transfer learning is a well-known solution to the problem of domain shift in which source domain (training set) and target domain (test set) are drawn from di fferent distributions. In the absence of domain shift, discriminative dimensionality reduction approaches could classify target data with acceptable accuracy. However, distribution diff erence across source and target domains degrades the performance of dimensionality
reduction methods. In this paper, we propose a Discriminative Dimensionality Reduction approach for multi-source Transfer learning, DiReT, in which discrimination is exploited on transferred data. DiReT nds an embedded space, such that the distribution di erence
of the source and target domains is minimized. Moreover, DiReT employs multiple source
domains and semi-supervised target domain to transfer knowledge from multiple resources,
and it also bridges across source and target domains to nd common knowledge in an
embedded space. Empirical evidence of real and arti cial datasets indicates that DiReT
manages to improve substantially over dimensionality reduction approaches.

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