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.

Documentation

Manual: dml.pdf
Vignette: None available.

Maintainer: Yuan Tang <terrytangyuan at gmail.com>

Author(s): Yuan Tang <terrytangyuan at gmail.com>, Gao Tao <joegaotao at gmail.com>, Xiao Nan <road2stat at gmail.com>

Install package and any missing dependencies by running this line in your R console:

install.packages("dml")

Depends MASS
Imports lfda
Suggests testthat
Enhances
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Package dml
Materials
URL https://github.com/terrytangyuan/dml
Task Views
Version 1.1.0
Published 2015-08-29
License MIT + file LICENSE
BugReports https://github.com/terrytangyuan/dml/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks dml check results
Package source dml_1.1.0.tar.gz