Transfer Learning by Structural Analogy

Transfer learning allows knowledge to be extracted from auxiliary domains and be used to enhance learning in a target domain. For transfer learning to be successful, it is critical to find the similarity between auxiliary and target domains, even when such mappings are not obvious. In this paper, we present a novel algorithm for finding the structural similarity between two domains, to enable transfer learning at a structured knowledge level. In particular, we address the problem of how to learn a non-trivial structural similarity mapping between two different domains when they are completely different on the representation level. This problem is challenging because we cannot directly compare features across domains. Our algorithm extracts the structural features within each domain and then maps the features into the Reproducing Kernel Hilbert Space (RKHS), such that the “structural dependencies” of features across domains can be estimated by kernel matrices of the features with...

• Type: Journal
• Year: 2011
• Where: AAAI
• Authors: Hua-Yan Wang, Qiang Yang
• Keywords: AAAI 2011, Intelligent Agents, Kernel Hilbert Space, Target Domain, Target Domains