Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 29, Issue 7, Pages 1494-1507Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2017.2685597
Keywords
Online transfer learning; multiple source domains; heterogeneous transfer
Categories
Funding
- Guangzhou Key Laboratory of Robotics and Intelligent Software [15180007]
- National Natural Science Foundation of China (NSFC) [61502177, 61403088, 61602185]
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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.
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