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Author Funatsu, N. ♦ Kuroki, Y.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Graphics processing unit ♦ Correlation ♦ Principal component analysis ♦ Face recognition ♦ Vectors ♦ Feature extraction ♦ Robustness
Abstract In data-analysis problems with a large number of dimensions, the principal component analysis based on L2-norm (L2-PCA) is one of the most popular methods, but L2-PCA is sensitive to outliers. Unlike L2-PCA, PCA-L1 is robust to outliers because it utilizes the L1-norm, which is less sensitive to outliers; therefore, some studies have shown the superiority of PCA-L1 to L2-PCA [2][3]. However, PCA-L1 requires enormous computational cost to obtain the bases, because PCA-L1 employs an iterative algorithm, and initial bases are eigenvectors of autocorrelation matrix. The autocorrelation matrix in the PCA-L1 needs to be recalculated for the each basis besides. In previous works [3], the authors proposed a fast PCA-L1 algorithm providing identical bases in terms of theoretical approach, and decreased computational time roughly to a quarter. This paper attempts to accelerate the computation of the L1-PCA bases using GPU.
Description Author affiliation: Kurume National College of Technology, 1-1-1, Komorino, Kurume-shi, Fukuoka 830-8555 Japan (Funatsu, N.; Kuroki, Y.)
ISBN 9781424468898
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-11-21
Publisher Place Japan
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781424468904
Size (in Bytes) 307.27 kB
Page Count 4
Starting Page 2087
Ending Page 2090

Source: IEEE Xplore Digital Library