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山东大学学报(工学版) ›› 2012, Vol. 42 ›› Issue (3): 31-38.

• 机器学习与数据挖掘 • 上一篇    下一篇

基于模糊隶属度的加权广义不定核判别分析

杨静,范丽亚   

  1. 聊城大学数学科学学院, 山东 聊城 252059
  • 收稿日期:2011-09-04 出版日期:2012-06-20 发布日期:2011-09-04
  • 作者简介:杨静(1986- ), 女, 山东临清人, 硕士研究生, 主要研究方向为模式识别. E-mail: yangjing860204@163.com
  • 基金资助:

    国家自然科学基金资助项目(10871226); 山东省自然科学基金资助项目(ZR2009AL006); 山东省中青年科学家科研奖励基金资助项目(BS2010SF004)

Weighted generalized indefinite kernel discriminant analysis based on  fuzzy memberships

YANG Jing, FAN Li-ya   

  1. School of Mathematics Sciences, Liaocheng University, Liaocheng 252059, China
  • Received:2011-09-04 Online:2012-06-20 Published:2011-09-04

摘要:

For linear non-separated problems, many dimensionality reduction methods which are based on the definite kernel were proposed. The Fisher discriminant analysis method,  one of the commonly used methods,  was improved and extended. The definite kernel was extended to the indefinite kernel, and then indefinite kernel discriminant analysis based on fuzzy memberships was proposed. In addition, weighted generalized IKDA algorithms were achieved according to the weighting function. Experimental results showed that the proposed methods could achieve good classification results, and the choice of the weighting function could have a significant effect on the classification results.

关键词: indefinite kernel, fuzzy discriminant analysis, fuzzy membership, weighting function, misclassification rate

Abstract:

For linear non-separated problems, many dimensionality reduction methods which are based on the definite kernel were proposed. The Fisher discriminant analysis method,  one of the commonly used methods,  was improved and extended. The definite kernel was extended to the indefinite kernel, and then indefinite kernel discriminant analysis based on fuzzy memberships was proposed. In addition, weighted generalized IKDA algorithms were achieved according to the weighting function. Experimental results showed that the proposed methods could achieve good classification results, and the choice of the weighting function could have a significant effect on the classification results.

Key words: indefinite kernel, fuzzy discriminant analysis, fuzzy membership, weighting function, misclassification rate

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