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TNN
2011

Domain Adaptation via Transfer Component Analysis

11 years 6 months ago
Domain Adaptation via Transfer Component Analysis
Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a Reproducing Kernel Hilbert Space (RKHS) using Maximum Mean Discrepancy (MMD). In the subspace spanned by these transfer components, data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. The main contribution of our work is that we propose a novel feature representation in which to perform domain adaptation via a new parametric kernel using feature extractio...
Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, Qi
Added 15 May 2011
Updated 15 May 2011
Type Journal
Year 2011
Where TNN
Authors Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, Qiang Yang
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