Online Transfer Learning with Multiple Homogeneous or Heterogeneous Sources

Transfer learning techniques have been broadly applied in applications where labeled data in a target domain are difficult to obtain while a lot of labeled data are available in related source domains. In practice, there can be multiple source domains that are related to the target domain, and how to combine them is still an open problem. In this paper, we seek to leverage labeled data from multiple source domains to enhance classification performance in a target domain where the target data are received in an online fashion. This problem is known as the online transfer learning problem. To achieve this, we propose novel online transfer learning paradigms in which the source and target domains are leveraged adaptively. We consider two different problem settings: homogeneous transfer learning and heterogeneous transfer learning. The proposed methods work in an online manner, where the weights of the source domains are adjusted dynamically. We provide the mistake bounds of the proposed methods and perform comprehensive experiments on real-world data sets to demonstrate the effectiveness of the proposed algorithms.

1 Introduction

Transfer learning is an important research topic in data mining and machine learning and has been extensively studied for many years [1]. The main objective of transfer learning is to make use of labeled data from one or multiple source domains to enhance the learning performance on a target domain in which labeled data for training are difficult to collect. Leveraging knowledge from the labeled source data can dramatically reduce expensive data-labeling efforts.