Learning with Augmented Features for Heterogeneous Domain Adaptation

TL;DR

This paper introduces a novel Heterogeneous Feature Augmentation (HFA) method for domain adaptation, transforming data into a common subspace and augmenting features to improve learning across different feature spaces.

Contribution

The paper presents a new HFA approach that combines feature transformation and augmentation, enabling existing models like SVM to effectively handle heterogeneous domain data.

Findings

HFA outperforms existing HDA methods on benchmark datasets.

The method effectively integrates with standard learning algorithms like SVM.

Kernelization allows handling high-dimensional data efficiently.

Abstract

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. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods.