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Scripts for the paper: Generalized 2-D principal component analysis by Lp-norm for image analysis.

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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

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Scripts for the paper: Generalized 2-D principal component analysis by Lp-norm for image analysis.

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