Supervised heterogeneous transfer learning using random forests

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.