收稿日期: 2013-12-25
修回日期: 2014-03-19
网络出版日期: 2015-01-15
基金资助
国家重点基础研究发展(973)计划(2014CB744600)、中国科学院重点项目(KJZD-EW-L04)、中国科学院战略性先导科技专项(XDA06030800)资助
Heterogeneous transfer learning based on translation invariant kernels
Received date: 2013-12-25
Revised date: 2014-03-19
Online published: 2015-01-15
关增达 , 程立 , 朱廷劭 . 基于平移不变核的异构迁移学习[J]. 中国科学院大学学报, 2015 , 32(1) : 121 -126 . DOI: 10.7523/j.issn.2095-6134.2015.01.020
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%.
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