Abstract:
Supervised transfer learning algorithms utilize labeled data from
auxiliary domains for learning in another domain where labeled
data is scarce or absent. Given sufficient cross-domain corresponding instances, one can learn a robust transformation that maps
the features across the domains by using any multi-output regression task. However, this cross-domain corresponding data is not
available for real-world transfer tasks across heterogeneous feature
spaces such as, cross-domain activity recognition and cross-lingual
text/sentiment classification. In this paper, we present a shared
label space driven algorithm that transfers labeled knowledge between heterogeneous feature spaces. The proposed algorithm treats
the similar label distributions across the domains as pivots to generate cross-domain corresponding data. The shared label distributions and the corresponding data is obtained from the random
forest models of the source and target domain. The experimental
results on synthetic and real-world benchmark datasets having
dissimilar modalities validate the performance of the proposed algorithm against state-of-the-art heterogeneous transfer learning
approaches.
