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 <email@example.com>, Gao Tao <firstname.lastname@example.org>, Xiao Nan <email@example.com>|
|Maintainer||Yuan Tang <firstname.lastname@example.org>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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