dml: Distance Metric Learning in R

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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.

Author
Yuan Tang <terrytangyuan@gmail.com>, Gao Tao <joegaotao@gmail.com>, Xiao Nan <road2stat@gmail.com>
Date of publication
2015-08-29 13:14:59
Maintainer
Yuan Tang <terrytangyuan@gmail.com>
License
MIT + file LICENSE
Version
1.1.0
URLs

View on CRAN

Man pages

dca
Discriminative Component Analysis
GdmDiag
Global Distance Metric Learning
GdmFull
Global Distance Metric Learning
rca
Relevant Component Analysis

Files in this package

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