Factor Analysis Algorithm with Mercer Kernel

Author

Xia Guo-en ; Shao Pei-ji

Author_Institution

Univ. of Electron. Sci. & Technol., Chengdu

fYear

2009

fDate

23-25 Jan. 2009

Firstpage

202

Lastpage

205

Abstract

Nonlinear factor analysis method was studied by Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and a comparison with the related method kernel principle component analysis (KPCA) was made. It is pointed that the best error rate in handwritten digit recognition by kernel factor analysis (KFA) with varimax (4.2%) is competitive with KPCA (4.4%). The results indicate that KFA with varimax could more accurately image handwritten digit recognition and could be an effective measure for studying pattern recognition.

Keywords

handwriting recognition; image recognition; principal component analysis; support vector machines; Mercer kernel function; high-dimensional feature space; image handwritten digit recognition; kernel principle component analysis; nonlinear kernel factor analysis algorithm; support vector machine; Algorithm design and analysis; Functional analysis; Handwriting recognition; Image recognition; Information analysis; Kernel; Pattern recognition; Performance analysis; Space stations; Space technology; Kernel factor analysis(KFA); Kernel principal component analysis(KPCA); Support vector machine (SVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Technology and Security Informatics, 2009. IITSI '09. Second International Symposium on

Conference_Location

Moscow

Print_ISBN

978-1-4244-3580-7

Type

conf

DOI

10.1109/IITSI.2009.55

Filename

4777581