Journal of University of Chinese Academy of Sciences >
Heterogeneous transfer learning based on translation invariant kernels
Received date: 2013-12-25
Revised date: 2014-03-19
Online published: 2015-01-15
We propose a new heterogeneous transfer learning method, which uses related heterogeneous feature dataset. We use translation invariant kernels(Euclidean kernels and RBF kernels) to map the target dataset and the related dataset to a new reproducing kernel Hilbert space, in which the two datasets have equal feature dimensions and similar distributions and reserve their topological property. The experimental results show that our method works well and the method based on the Euclidean kernel improves accuracy by more than 5%~10%.
GUAN Zengda , CHENG Li , ZHU Tingshao . Heterogeneous transfer learning based on translation invariant kernels[J]. Journal of University of Chinese Academy of Sciences, 2015 , 32(1) : 121 -126 . DOI: 10.7523/j.issn.2095-6134.2015.01.020
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