核分布一致局部领域适应学习

陶剑文; 王士同

doi:10.3724/SP.J.1004.2013.01295

核分布一致局部领域适应学习

doi: 10.3724/SP.J.1004.2013.01295

陶剑文^1,2,
王士同¹

1.
江南大学数字媒体学院无锡 214122;
2.
浙江工商职业技术学院电子与信息工程学院宁波 315012

基金项目:

国家自然科学基金(60975027, 60903100);教育部人文社会科学研究规划基金(13YJAZH084);浙江省自然科学基金(LY13F020011)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, 机器学习.E-mail: wxwangst@yahoo.com.cn

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- 文章访问数: 1720
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- 被引次数: 0
出版历程
- 收稿日期: 2011-10-28
- 修回日期: 2012-03-19
- 刊出日期: 2013-08-20

Kernel Distribution Consistency Based Local Domain Adaptation Learning

TAO Jian-Wen^1,2,
WANG Shi-Tong¹

1.
School of Digital Media, Southern Yangtze University, Wuxi 214122;
2.
School of Electronics and Information Engineering, Zhejiang Business Technology Institute, Ningbo 315012

Funds:

Supported by National Natural Science Foundation of China (60975027, 60903100), Humanities and Social Sciences Research Fund of Ministry of Education (13YJAZH084), and Natural Science Foundation of Zhejiang Province (LY13F020011)

摘要

摘要: 针对领域适应学习(Domain adaptation learning, DAL)问题,提出一种核分布一致局部领域适应学习机(Kernel distribution consistency based local domain adaptation classifier, KDC-LDAC),在某个通用再生核Hilbert空间(Universally reproduced kernel Hilbert space, URKHS),基于结构风险最小化模型, KDC-LDAC首先学习一个核分布一致正则化支持向量机(Support vector machine, SVM),对目标数据进行初始划分; 然后,基于核局部学习思想,对目标数据类别信息进行局部回归重构; 最后,利用学习获得的类别信息,在目标领域训练学习一个适于目标判别的分类器.人造和实际数据集实验结果显示,所提方法具有优化或可比较的领域适应学习性能.
- 领域适应学习 /
- 核分布一致 /
- 局部学习 /
- 模式分类 /
- 最大平均差
Abstract: In allusion to domain adaptation learning (DAL) problems, this paper proposes a novel so-called kernel distribution consistency based local domain adaptation classifier (KDC-LDAC). Firstly, in some universally reproduced kernel Hilbert space (URKHS), the KDC-LDAC trains a kernel distribution consistency regularized domain adaptation support vector machine (SVM) based on the structure risk minimization model, which extends the formulation of classical SVMs to the domain adaptation learning schema. And secondly, according to the idea of local learning, the proposed method predicts the label of each data point in target domain based on its neighbors and their labels in the URKHS. The last but not least, the KDC-LDACs learning a discriminant function to classify the unseen data in target domain with training data well predicted in the local learning procedure. Experimental results on artificial and real world problems show the advantages or comparable effectiveness of the proposed approach compared to related approaches.
- Domain adaptation learning (DAL) /
- kernel distribution consistency (KDC) /
- local learning /
- pattern classification /
- maximum mean discrepancy (MMD)

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