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Kernel Factor Analysis Algorithm with Varimax

Kernel Factor Analysis Algorithm with Varimax
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摘要 Kernal factor analysis （KFA） with vafimax 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 vadmax （4.2%） was superior to KPCA （4.4%）. The KFA with varimax could more accurately image handwritten digit recognition. Kernal factor analysis （KFA） with vafimax 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 vadmax （4.2%） was superior to KPCA （4.4%）. The KFA with varimax could more accurately image handwritten digit recognition.
作者 夏国恩 金炜东 张葛祥
机构地区 School of Economics and Management School of Electrical Engineering
出处 《Journal of Southwest Jiaotong University(English Edition)》 2006年第4期394-399,共6页 西南交通大学学报（英文版）
基金 The National Defence Foundation of China (No.NEWL51435Qt220401)
关键词 Kernel factor analysis Kernel principal component analysis Support vector machine Varimax ALGORITHM Handwritten digit recognition Kernel factor analysis Kernel principal component analysis Support vector machine Varimax Algorithm Handwritten digit recognition
分类号 O221 [理学—运筹学与控制论]
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参考文献13

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Journal of Southwest Jiaotong University(English Edition)
2006年 第4期

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