Distance metric is widely used in the machine learning literature. We used to choose a distance metric according to a priori (Euclidean Distance , L1 Distance, etc.) or according to the result of cross validation within small class of functions (e.g. choosing order of polynomial for a kernel). Actually, with priori knowledge of the data, we could learn a more suitable distance metric with (semi-)supervised distance metric learning techniques. sdml is such an R package aims to implement the state-of-the-art algorithms for supervised distance metric learning. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.

Algorithms planned in the first development stage:

Supervised Global Distance Metric Learning:

- Relevant Component Analysis (RCA) - implemented
- Kernel Relevant Component Analysis (KRCA)
- Discriminative Component Analysis (DCA) - implemented
- Kernel Discriminative Component Analysis (KDCA)
- Global Distance Metric Learning by Convex Programming - implemented

Supervised Local Distance Metric Learning:

- Local Fisher Discriminant Analysis - implemented
- Kernel Local Fisher Discriminant Analysis - implemented
- Information-Theoretic Metric Learning (ITML)
- Large Margin Nearest Neighbor Classifier (LMNN)
- Neighbourhood Components Analysis (NCA)
- Localized Distance Metric Learning (LDM)

The algorithms and routines might be adjusted during developing.

Track Devel: https://github.com/terrytangyuan/dml

Report Bugs: https://github.com/terrytangyuan/dml/issues

Contact the maintainer of this package: Yuan Tang terrytangyuan@gmail.com