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 |
| DOI: | 10.32614/CRAN.package.dml |
| Author: | Yuan Tang, Gao Tao, Xiao Nan |
| Maintainer: | Yuan Tang <terrytangyuan at gmail.com> |
| BugReports: | https://github.com/terrytangyuan/dml/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/terrytangyuan/dml |
| 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: dml_1.1.0.zip, r-release: dml_1.1.0.zip, r-oldrel: dml_1.1.0.zip |
| macOS binaries: | r-release (arm64): dml_1.1.0.tgz, r-oldrel (arm64): dml_1.1.0.tgz, r-release (x86_64): dml_1.1.0.tgz, r-oldrel (x86_64): dml_1.1.0.tgz |
| Reverse imports: | ssPATHS |
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