  Transfer learning aims to learn classifiers for a target domain by
transferring knowledge from a source domain. However, due to two main issues:
feature discrepancy and distribution divergence, transfer learning can be a
very difficult problem in practice. In this paper, we present a framework
called TLF that builds a classifier for the target domain having only few
labeled training records by transferring knowledge from the source domain
having many labeled records. While existing methods often focus on one issue
and leave the other one for the further work, TLF is capable of handling both
issues simultaneously. In TLF, we alleviate feature discrepancy by identifying
shared label distributions that act as the pivots to bridge the domains. We
handle distribution divergence by simultaneously optimizing the structural risk
functional, joint distributions between domains, and the manifold consistency
underlying marginal distributions. Moreover, for the manifold consistency we
exploit its intrinsic properties by identifying k nearest neighbors of a
record, where the value of k is determined automatically in TLF. Furthermore,
since negative transfer is not desired, we consider only the source records
that are belonging to the source pivots during the knowledge transfer. We
evaluate TLF on seven publicly available natural datasets and compare the
performance of TLF against the performance of fourteen state-of-the-art
techniques. We also evaluate the effectiveness of TLF in some challenging
situations. Our experimental results, including statistical sign test and
Nemenyi test analyses, indicate a clear superiority of the proposed framework
over the state-of-the-art techniques.
