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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.
| Version: | 1.1.0 |
| Depends: | MASS |
| Imports: | lfda |
| Suggests: | testthat |
| Published: | 2015-08-29 |
| 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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