dml: Distance Metric Learning in R

The state-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.

Version:	1.1.0
Depends:	MASS
Imports:	lfda
Suggests:	testthat
Published:	2015-08-29
DOI:	10.32614/CRAN.package.dml
Author:	Yuan Tang, Gao Tao, Xiao Nan
Maintainer:	Yuan Tang <terrytangyuan at gmail.com>
BugReports:	https://github.com/terrytangyuan/dml/issues
License:	MIT + file LICENSE
URL:	https://github.com/terrytangyuan/dml
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	dml results

Documentation:

Reference manual:

dml.html , dml.pdf

Downloads:

Package source:	dml_1.1.0.tar.gz
Windows binaries:	r-devel: dml_1.1.0.zip, r-release: dml_1.1.0.zip, r-oldrel: dml_1.1.0.zip
macOS binaries:	r-release (arm64): dml_1.1.0.tgz, r-oldrel (arm64): dml_1.1.0.tgz, r-release (x86_64): dml_1.1.0.tgz, r-oldrel (x86_64): dml_1.1.0.tgz

Reverse dependencies:

Reverse imports:

ssPATHS

Linking:

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