基于共同决策方向矢量的多源迁移及其快速学习方法

doi:10.3969/j.issn.0372-2112.2015.07.015

电子学报 ›› 2015, Vol. 43 ›› Issue (7): 1349-1355.DOI: 10.3969/j.issn.0372-2112.2015.07.015

基于共同决策方向矢量的多源迁移及其快速学习方法

张景祥^1,2, 王士同¹

1. 江南大学数字媒体学院, 江苏无锡 214122;
2. 江南大学理学院, 江苏无锡 214122

收稿日期:2014-01-07 修回日期:2014-08-25 出版日期:2015-07-25
- 作者简介:
- 张景祥 男,1977年生于吉林通化.2002年、2007年分别在重庆大学、江南大学获得理学学士、工学硕士学位,2011年江南大学攻读博士学位,主要从事模式识别、人工智能和生物信息学等方面的研究. E-mail:zjx145@163.com;王士同 男,1964年生于江苏扬州.教授、博士生导师、中国计算机学会高级会员.1984年、1987年在南京航空航天大学获得工学学士、硕士学位.主要从事人工智能、模式识别、模糊系统、医学图像处理和生物信息学等方面的研究工作.
- 基金资助:
- 国家自然科学基金 (No.61202311,No.61272210); 江南大学博士科研基金 (No.JUDCF13031); 江苏省普通高校研究生科研创新计划基金 (No.CXLX13-748)

Common-Decision-Vector Based Multiple Source Transfer Learning Classification and Its Fast Learning Method

ZHANG Jing-xiang^1,2, WANG Shi-tong¹

1. School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, China;
2. School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China

Received:2014-01-07 Revised:2014-08-25 Online:2015-07-25 Published:2015-07-25

摘要/Abstract

摘要：

多源迁移学习提取了多个相似领域之间有用信息,提高了学习效率,但存在计算核矩阵的空间和时间复杂度较高的问题.提出了一种多源迁移学习方法,该方法基于结构风险最小框架理论,以共同决策方向矢量为基准,将多个相似领域的决策方向矢量嵌入到支持向量机的训练过程中,提高了目标领域分类器的分类性能.并结合核心向量机理论提出了共同决策方向矢量核心向量机,实现对大样本数据集的快速分类学习.模拟和真实数据集实验表明了所提算法的有效性.

关键词: 共同决策矢量, 多源迁移学习, 分类, 核心集向量机

Abstract:

Multiple source transfer learning (MSTL) has been obtaining more and more applications especially from several related source domains to help the learning task on target domain.However,multiple source transfer learning algorithms often deal with the corresponding quadratic programming problems which may suffer a big computational burden caused by the kernel matrix computation.In this paper,a novel common-decision-vector based multiple source transfer classification learning (CDV-MSTL) is proposed which doesn't depend on the intrinsic structure of data.This algorithm is based on the structural risk minimization principle and the SVM like framework,so it has good adaptability and better accuracy.Based on the theory of CVM,CDV-MSTL is extended to its CVM based version which can realize fast training for large scale data.Extensive experiments on synthetic and real-world datasets demonstrate the significant improvement in classification performance obtained by the proposed algorithm over existing MSTL algorithm.

Key words: common decision vector, multiple source transfer learning, classification, core vector machine

中图分类号:

TP391.4

张景祥, 王士同. 基于共同决策方向矢量的多源迁移及其快速学习方法[J]. 电子学报, 2015, 43(7): 1349-1355.

ZHANG Jing-xiang, WANG Shi-tong. Common-Decision-Vector Based Multiple Source Transfer Learning Classification and Its Fast Learning Method[J]. Acta Electronica Sinica, 2015, 43(7): 1349-1355.

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基于共同决策方向矢量的多源迁移及其快速学习方法

Common-Decision-Vector Based Multiple Source Transfer Learning Classification and Its Fast Learning Method

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