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.

AuthorYuan Tang <terrytangyuan@gmail.com>, Gao Tao <joegaotao@gmail.com>, Xiao Nan <road2stat@gmail.com>
Date of publication2015-08-29 13:14:59
MaintainerYuan Tang <terrytangyuan@gmail.com>
LicenseMIT + file LICENSE
Version1.1.0
https://github.com/terrytangyuan/dml

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Files

dml
dml/tests
dml/tests/testthat.R
dml/tests/testthat
dml/tests/testthat/test_helper_functions.R
dml/tests/testthat/test_algorithms.R
dml/NAMESPACE
dml/NEWS
dml/R
dml/R/gdmf.r
dml/R/aaa.R dml/R/rca.R dml/R/dca.R
dml/R/gdmd.r
dml/README.md
dml/MD5
dml/DESCRIPTION
dml/man
dml/man/rca.Rd dml/man/GdmFull.Rd dml/man/GdmDiag.Rd dml/man/dca.Rd
dml/LICENSE

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