Abstract:
The elicitation of labeled training data from physical sources is a
primary bottleneck that limits the applicability of traditional supervised
learning algorithms. Transfer learning algorithms overcome
the limitation for a target domain where training data is scarce or
absent by leveraging labeled data from related auxiliary domains.
The proposed novel transfer learning frameworks yield a sparse
and linear transformation to bridge domains with heterogeneous
features without relying on instance or feature correspondences.
Extensive experiments are conducted on real-world heterogeneous
transfer tasks with diverse datasets of varying dimensions and sparsity
to investigate the effectiveness of the proposed approaches
over other baseline and state of the art transfer approaches.
