Title: Heterogeneous Transfer Learning: An Unsupervised Approach,
Abstract: Transfer learning leverages the knowledge in one domain, the source domain,
to improve learning efficiency in another domain, the target domain. Existing
transfer learning research is relatively well-progressed, but only in
situations where the feature spaces of the domains are homogeneous and the
target domain contains at least a few labeled instances. However, transfer
learning has not been well-studied in heterogeneous settings with an unlabeled
target domain. To contribute to the research in this emerging field, this paper
presents: (1) an unsupervised knowledge transfer theorem that prevents negative
transfer; and (2) a principal angle-based metric to measure the distance
between two pairs of domains. The metric shows the extent to which homogeneous
representations have preserved the information in original source and target
domains. The unsupervised knowledge transfer theorem sets out the transfer
conditions necessary to prevent negative transfer. Linear monotonic maps meet
the transfer conditions of the theorem and, hence, are used to construct
homogeneous representations of the heterogeneous domains, which in principle
prevents negative transfer. The metric and the theorem have been implemented in
an innovative transfer model, called a Grassmann-LMM-geodesic flow kernel
(GLG), that is specifically designed for knowledge transfer across
heterogeneous domains. The GLG model learns homogeneous representations of
heterogeneous domains by minimizing the proposed metric. Knowledge is
transferred through these learned representations via a geodesic flow kernel.
Notably, the theorem presented in this paper provides the sufficient transfer
conditions needed to guarantee that knowledge is transferred from a source
domain to an unlabeled target domain with correctness.