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Scripts for the paper: Generalized 2-D principal component analysis by Lp-norm for image analysis.
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yuzhounh/G2DPCA_demo_2
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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 30 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 [email protected] [email protected] 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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