Comparative Performance Analysis of Three Algorithms for Principal Component Analysis
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Date
2006-12
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ORCID
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Referee
Mark
Journal Title
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Publisher
Společnost pro radioelektronické inženýrství
Abstract
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.
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Citation
Radioengineering. 2006, vol. 15, č. 4, s. 84-90. ISSN 1210-2512
http://www.radioeng.cz/fulltexts/2006/06_04_84_90.pdf
http://www.radioeng.cz/fulltexts/2006/06_04_84_90.pdf
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Peer-reviewed
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en