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
Author: Yuan Tang, Gao Tao, Xiao Nan
Maintainer: Yuan Tang <terrytangyuan at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: dml results


Reference manual: dml.pdf
Package source: dml_1.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: dml_1.1.0.tgz
OS X Mavericks binaries: r-oldrel: dml_1.1.0.tgz


Please use the canonical form to link to this page.