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Generalized two dimensional principal component analysis by Lp-norm for image analysis.
Copyright (C) 2018 Jing Wang
For 1D algorithms, the image data should be 2D matrix, nSub*(height*width).
For 2D algorithms, the image data should be 3D matrix, height*width*nSub.
The images are listed in the subject-by-subject manner. Please refer to the
manuscript for more information about the experiments.
Variables:
FaceDB, Feret or ORL
x, image data
rho, a tuning parameter in RSPCA or 2DPCAL1-S
s, a tuning parameter in GPCA or G2DPCA
p, a tuning parameter in GPCA or G2DPCA
nPV, number of projection vectors to be calculated
W, projection vectors
Usage:
Run main.m to play this demo. It takes about 23 hours on a server with 40 CPUs.
Related codes:
2DPCAL1-S, https://github.com/yuzhounh/2DPCAL1-S
G2DPCA, https://github.com/yuzhounh/G2DPCA
G2DPCA_demo_1, https://github.com/yuzhounh/G2DPCA_demo_1
G2DPCA_demo_2, https://github.com/yuzhounh/G2DPCA_demo_2
Contact information:
Jing Wang
wangjing0@seu.edu.cn
yuzhounh@163.com
2018-6-13 22:58:14