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:
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Supervised Global Distance Metric Learning:
- Relevant Component Analysis (RCA)
- Discriminative Component Analysis (DCA)
- Kernel Discriminative Component Analysis (KDCA)
- Global Distance Metric Learning by Convex Programming (GDMLCP)
- Probablistic Global Distance Metric Learning (PGDM)
-
Supervised Local Distance Metric Learning:
- Local Fisher Discriminant Analysis (LFDA)
- Kernel Local Fisher Discriminant Analysis (KLFDA)
- Localized Distance Metric Learning (LDM)
- Information-Theoretic Metric Learning (ITML)
- Neighbourhood Components Analysis (NCA)
- Large Margin Nearest Neighbor Classifier (LMNN)
The algorithms and routines might be adjusted during developing.
Track Devel: https://github.com/road2stat/sdml
Report Bugs: https://github.com/road2stat/sdml/issues
Contact the authors of this package:
Gao Tao joegaotao@gmail.com
Xiao Nan road2stat@gmail.com