Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation

Abstract

In this paper, we study the heterogeneous domain adaptation (HDA) problem, in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. By introducing two different projection matrices, we first transform the data from two domains into a common subspace such that the similarity between samples across different domains can be measured. We then propose a new feature mapping function for each domain, which augments the transformed samples with their original features and zeros. Existing supervised learning methods (e.g., SVM and SVR) can be readily employed by incorporating our newly proposed augmented feature representations for supervised HDA. As a showcase, we propose a novel method called Heterogeneous Feature Augmentation (HFA) based on SVM. We show that the proposed formulation can be equivalently derived as a standard Multiple Kernel Learning (MKL) problem, which is convex and thus the global solution can be guaranteed. To additionally utilize the unlabeled data in the target domain, we further propose the semi-supervised HFA (SHFA) which can simultaneously learn the target classifier as well as infer the labels of unlabeled target samples. Comprehensive experiments on three different applications clearly demonstrate that our SHFA and HFA outperform the existing HDA methods.

Learning from the Mistakes of Others: Matching Errors in Cross-Dataset Learning

“…[25]) and domain adaptation (e.g. [21,40]). Quadrianto et al [25] matched the distribution between function outputs on the training data f (X) := {f (x 1 ), .…”

Heterogeneous transfer learning techniques for machine learning

“…In heterogeneous transfer learning, the source and target domains represented different features spaces; there are so many applications, where the heterogeneous transfer learning is applicable and useful, like in the following areas, image recognition [1][2][3][4][5][6], the multi languages text classification [1,[6][7][8][9][10], single languages text classification [11], drugs classification [4], the human activity classification [12], and software defect classification [13].…”

Learning Discriminative Correlation Subspace for Heterogeneous Domain Adaptation

“…However, the collection and annotation of data can be very expensive and time-consuming. To tackle this issue, domain adaptation (DA) is proposed to utilize auxiliary data from another domain, a.k.a., source domain, to enhance the performance of the target task [Pan and Yang, 2010;Patel et al, 2015]. DA has been successfully applied in many real-world applications, including text classification [Chen and Zhang, 2013], visual recognition [Duan et al, 2010;2012b], * The co-first authors.…”