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 protected]>, Tao Gao <[email protected]>, Nan Xiao <[email protected]>|
|Maintainer||Yuan Tang <[email protected]>|
|License||MIT + file LICENSE|
|Package repository||View on GitHub|
Install the latest version of this package by entering the following in R:
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.