作者
Chang Wang, Sridhar Mahadevan
发表日期
2011/7/16
期刊
IJCAI proceedings-international joint conference on artificial intelligence
卷号
22
期号
1
页码范围
1541
简介
We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications.
引用总数
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C Wang, S Mahadevan - IJCAI proceedings-international joint conference on …, 2011