Learning with Augmented Features for Heterogeneous Domain Adaptation

Understand

We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions.

• Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains.
• We then propose two new feature mapping functions to augment the transformed data with their original features and zeros.
• The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA.