kfda: Kernel Fisher Discriminant Analysis

Kernel Fisher Discriminant Analysis (KFDA) is performed using Kernel Principal Component Analysis (KPCA) and Fisher Discriminant Analysis (FDA). There are some similar packages. First, 'lfda' is a package that performs Local Fisher Discriminant Analysis (LFDA) and performs other functions. In particular, 'lfda' seems to be impossible to test because it needs the label information of the data in the function argument. Also, the 'ks' package has a limited dimension, which makes it difficult to analyze properly. This package is a simple and practical package for KFDA based on the paper of Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. (2004) <doi:10.1016/j.patcog.2003.10.015>.

Version:	1.0.0
Depends:	R (≥ 3.0.0), kernlab, MASS
Published:	2017-09-27
Author:	Donghwan Kim
Maintainer:	Donghwan Kim <donhkim9714 at korea.ac.kr>
License:	GPL-3
URL:	https://github.com/ainsuotain/kfda
NeedsCompilation:	no
CRAN checks:	kfda results

Documentation:

Reference manual:

kfda.pdf

Downloads:

Package source:	kfda_1.0.0.tar.gz
Windows binaries:	r-devel: kfda_1.0.0.zip, r-release: kfda_1.0.0.zip, r-oldrel: kfda_1.0.0.zip
macOS binaries:	r-release (arm64): kfda_1.0.0.tgz, r-oldrel (arm64): kfda_1.0.0.tgz, r-release (x86_64): kfda_1.0.0.tgz, r-oldrel (x86_64): kfda_1.0.0.tgz

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