- Session 3: Supervised and General Machine Learning -- Day 3 (Nov.19), talks: 10:50-11:30 (5th floor Hall 1), poster session: 11:30-14:00
- Poster number: Tue04
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Masato Ishii (The University of Tokyo/RIKEN/NEC); Takashi Takenouchi (Future University Hakodate/RIKEN Center for Advanced Intelligence Project); Masashi Sugiyama (RIKEN/The University of Tokyo)
In this paper, we propose a novel domain adaptation method that can be applied without target data. We consider the situation where domain shift is caused by a prior change of a specific factor and assume that we know how the prior changes between source and target domains. We call this factor an attribute, and reformulate the domain adaptation problem to utilize the attribute prior instead of target data. In our method, the source data are reweighted with the sample-wise weight estimated by the attribute prior and the data themselves so that they are useful in the target domain. We theoretically reveal that our method provides more precise estimation of sample-wise transferability than a straightforward attribute-based reweighting approach. Experimental results with both toy datasets and benchmark datasets show that our method can perform well, though it does not use any target data.