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Kernel Factor Analysis Algorithm with Varimax

Xia Guoen Jin Weidong Zhang Gexiang

西南交通大学学报（英文版）2006，Vol.14Issue(4)：394-399,6.

Kernel Factor Analysis Algorithm with Varimax

Xia Guoen ¹Jin Weidong ²Zhang Gexiang²

作者信息

1. School of Economics and Management, Southwest Jiaotong University, Chendu 610031, China
2. School of Electrical Engineering, Southwest Jiaotong University , Chendu 610031, China
折叠

摘要

Abstract

Kernal factor analysis (KFA) with varimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis (KPCA). The results show that the best error rate in handwritten digit recognition by kernel factor analysis with varimax (4.2%) was superior to KPCA (4.4%). The KFA with varimax could more accurately image handwritten digit recognition.

关键词

Kernel factor analysis/Kernel principal component analysis/Support vector machine/Varimax/Algorithm/Handwritten digit recognition

Key words

Kernel factor analysis/Kernel principal component analysis/Support vector machine/Varimax/Algorithm/Handwritten digit recognition

分类

社会科学

引用本文复制引用

Xia Guoen ,Jin Weidong ,Zhang Gexiang..Kernel Factor Analysis Algorithm with Varimax[J].西南交通大学学报（英文版）,2006,14(4):394-399,6.

基金项目

The National Defence Foundation of China (No. NEWL51435Qt220401) （No. NEWL51435Qt220401）

西南交通大学学报（英文版）

ISSN：2662-4745

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