Fast principal component analysis using fixed-point algorithm
Author(s)
Sharma, Alok
Pahwal, Kuldip K
Griffith University Author(s)
Year published
2007
Metadata
Show full item recordAbstract
In this paper we present an efficient way of computing principal component analysis (PCA). The algorithm finds the desired number of leading eigenvectors with a computational cost that is much less than that from the eigenvalue decomposition (EVD) based PCA method. The mean squared error generated by the proposed method is very similar to the EVD based PCA method.In this paper we present an efficient way of computing principal component analysis (PCA). The algorithm finds the desired number of leading eigenvectors with a computational cost that is much less than that from the eigenvalue decomposition (EVD) based PCA method. The mean squared error generated by the proposed method is very similar to the EVD based PCA method.
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Journal Title
Pattern Recognition Letters
Volume
28
Publisher URI
Subject
Cognitive and computational psychology