DocumentCode
698333
Title
Effect of normalization of eigenvectors on the past and RP algorithms for PCA
Author
Landqvist, R. ; Mohammed, A.
Author_Institution
Sch. of Eng., Blekinge Inst. of Technol., Ronneby, Sweden
fYear
2005
fDate
4-8 Sept. 2005
Firstpage
1
Lastpage
4
Abstract
This paper investigates the effect of incorporating normalization of the eigenvectors between iterations in the PAST and RP algorithms for principal component analysis (PCA). In addition, an algorithm denoted as exact eigendecomposition (EE) is proposed for PCA. The algorithms are compared for different configurations using Monte Carlo simulations. Simulation results show that EE has the best performance and that normalization may be used for improving PAST and RP.
Keywords
Monte Carlo methods; eigenvalues and eigenfunctions; principal component analysis; signal processing; Monte Carlo simulations; PCA; eigenvectors; exact eigendecomposition; normalization; principal component analysis; Algorithm design and analysis; Correlation; Eigenvalues and eigenfunctions; Monte Carlo methods; Principal component analysis; Signal processing algorithms; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing Conference, 2005 13th European
Conference_Location
Antalya
Print_ISBN
978-160-4238-21-1
Type
conf
Filename
7077916
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