Multi-source transfer learning based on label shared subspace

Min Fang; Yong Guo; Xiaosong Zhang; Xiao Li

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Multi-source transfer learning based on label shared subspace

机译：基于标签共享子空间的多源转移学习

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摘要

Multi-source transfer learning focuses on studying the scarcity of samples with labels in target domain, while neglecting the analysis about transferability relationship among multiple source domains. Thus, we propose a method that transforms samples in target domain into multi-label samples, with which it is able to analyze the correlations among predicted labels from different sources. We design a method that can extract the shared subspace among labels in multi-sources, and propose a novel multi-source transfer learning method based on multi-label shared subspace. This approach is required when knowledge about multiple sources are available but it is unknown which source is of more transferability. Experiments show that our proposed algorithm can improve the performance of transfer learning method and alleviate time complexity.

机译：多源转移学习侧重于研究目标域中带有标签的样本的稀缺性，而忽略了对多个源域之间转移性关系的分析。因此，我们提出了一种将目标域中的样本转换为多标签样本的方法，利用该方法可以分析来自不同来源的预测标签之间的相关性。我们设计了一种可以提取多源标签间共享子空间的方法，并提出了一种基于多标签共享子空间的多源转移学习方法。当可以获得有关多个源的知识时，需要使用此方法，但不知道哪个源具有更高的可传递性。实验表明，本文提出的算法可以提高转移学习方法的性能，减轻时间复杂度。

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《Pattern recognition letters》 |2015年第1期|101-106|共6页
作者
Min Fang; Yong Guo; Xiaosong Zhang; Xiao Li;
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作者单位

School of Computer Science and Technology, Xidian University, Shaanxi 710071, China;

School of Computer Science and Technology, Xidian University, Shaanxi 710071, China;

Xi'an Highway Institute, Xi'an, Shaanxi 710075, China;

School of Computer Science and Technology, Xidian University, Shaanxi 710071, China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Transfer learning; Multi-source; Shared subspace of labels;

机译：转移学习;多源;标签的共享子空间;

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Multi-source transfer learning based on label shared subspace

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