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融合异构特征的子空间迁移学习算法

张景祥 王士同 邓赵红 蒋亦樟 李奕

张景祥, 王士同, 邓赵红, 蒋亦樟, 李奕. 融合异构特征的子空间迁移学习算法. 自动化学报, 2014, 40(2): 236-246. doi: 10.3724/SP.J.1004.2014.00236
引用本文: 张景祥, 王士同, 邓赵红, 蒋亦樟, 李奕. 融合异构特征的子空间迁移学习算法. 自动化学报, 2014, 40(2): 236-246. doi: 10.3724/SP.J.1004.2014.00236
ZHANG Jing-Xiang, WANG Shi-Tong, DENG Zhao-Hong, JIANG Yi-Zhang, LI Yi. A Subspace Transfer Learning Algorithm Integrating Heterogeneous Features. ACTA AUTOMATICA SINICA, 2014, 40(2): 236-246. doi: 10.3724/SP.J.1004.2014.00236
Citation: ZHANG Jing-Xiang, WANG Shi-Tong, DENG Zhao-Hong, JIANG Yi-Zhang, LI Yi. A Subspace Transfer Learning Algorithm Integrating Heterogeneous Features. ACTA AUTOMATICA SINICA, 2014, 40(2): 236-246. doi: 10.3724/SP.J.1004.2014.00236

融合异构特征的子空间迁移学习算法

doi: 10.3724/SP.J.1004.2014.00236
基金项目: 

国家自然科学基金(61170122,61202311,61272210);江苏省自然科学基金(BK2012552)资助

详细信息
    作者简介:

    王士同 江南大学数字媒体学院教授.主要研究方向为人工智能,模式识别和生物信息.E-mail:wxwangst@yahoo.com.cn

A Subspace Transfer Learning Algorithm Integrating Heterogeneous Features

Funds: 

Supported by National Natural Science Foundation of China (61170122, 61202311, 61272210), and Natural Science Foundation of Jiangsu Province (BK2012552)

  • 摘要: 特征迁移重在领域共有特征间学习,然而其忽略领域特有特征的判别信息,使算法的适应性受到一定的局限. 针对此问题,提出了一种融合异构特征的子空间迁移学习(The subspace transfer learning algorithm integrating with heterogeneous features,STL-IHF)算法.该算法将数据的特征空间看成共享和特有两个特征子空间的组合,同时基于经验风险最 小框架将共享特征和特有特征共同嵌入到支持向量机(Support vector machine,SVM)的训练过程中.其在共享特征子空间上实现知识迁移的 同时兼顾了领域特有的异构信息,增强了算法的适应性.模拟和真实数据集上的实验结果表明了所提方法的有效性.
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出版历程
  • 收稿日期:  2012-12-31
  • 修回日期:  2013-04-02
  • 刊出日期:  2014-02-20

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