Comparative Performance Analysis of Three Algorithms for Principal Component Analysis

Comparative Performance Analysis of Three Algorithms for Principal Component Analysis

Principal Component Analysis (PCA) is an important concept in statistical signal processing. In this paper, we evaluate an on-line algorithm for PCA, which we denote as the Exact Eigendecomposition (EE) algorithm. The algorithm is evaluated using Monte Carlo Simulations and compared with the PAST an...

Full description

Bibliographic Details
Main Authors: A. Mohammed, R. Landqvist
Format: Article
Language:English
Published: Spolecnost pro radioelektronicke inzenyrstvi 2006-12-01
Series:Radioengineering
Online Access:http://www.radioeng.cz/fulltexts/2006/06_04_84_90.pdf
id doaj-549ee26f02e74000b7452ec51a31ea18
record_format Article
spelling doaj-549ee26f02e74000b7452ec51a31ea182020-11-25T00:22:23ZengSpolecnost pro radioelektronicke inzenyrstviRadioengineering1210-25122006-12-011548490Comparative Performance Analysis of Three Algorithms for Principal Component AnalysisA. MohammedR. LandqvistPrincipal Component Analysis (PCA) is an important concept in statistical signal processing. In this paper, we evaluate an on-line algorithm for PCA, which we denote as the Exact Eigendecomposition (EE) algorithm. The algorithm is evaluated using Monte Carlo Simulations and compared with the PAST and RP algorithms. In addition, we investigate a normalization procedure of the eigenvectors for PAST and RP. The results show that EE has the best performance and that normalization improves the performance of PAST and RP algorithms, respectively.www.radioeng.cz/fulltexts/2006/06_04_84_90.pdf
collection DOAJ
language English
format Article
sources DOAJ
author A. Mohammed
R. Landqvist
spellingShingle A. Mohammed
R. Landqvist
Comparative Performance Analysis of Three Algorithms for Principal Component Analysis
Radioengineering
author_facet A. Mohammed
R. Landqvist
author_sort A. Mohammed
title Comparative Performance Analysis of Three Algorithms for Principal Component Analysis
title_short Comparative Performance Analysis of Three Algorithms for Principal Component Analysis
title_full Comparative Performance Analysis of Three Algorithms for Principal Component Analysis
title_fullStr Comparative Performance Analysis of Three Algorithms for Principal Component Analysis
title_full_unstemmed Comparative Performance Analysis of Three Algorithms for Principal Component Analysis
title_sort comparative performance analysis of three algorithms for principal component analysis
publisher Spolecnost pro radioelektronicke inzenyrstvi
series Radioengineering
issn 1210-2512
publishDate 2006-12-01
description Principal Component Analysis (PCA) is an important concept in statistical signal processing. In this paper, we evaluate an on-line algorithm for PCA, which we denote as the Exact Eigendecomposition (EE) algorithm. The algorithm is evaluated using Monte Carlo Simulations and compared with the PAST and RP algorithms. In addition, we investigate a normalization procedure of the eigenvectors for PAST and RP. The results show that EE has the best performance and that normalization improves the performance of PAST and RP algorithms, respectively.
url http://www.radioeng.cz/fulltexts/2006/06_04_84_90.pdf
work_keys_str_mv AT amohammed comparativeperformanceanalysisofthreealgorithmsforprincipalcomponentanalysis
AT rlandqvist comparativeperformanceanalysisofthreealgorithmsforprincipalcomponentanalysis
_version_ 1725360139398021120

Similar Items