new package, which will be maintained actively.Note: This package has been moved to a
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 an R package aiming 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)
- Kernel Relevant Component Analysis (KRCA)
- Discriminative Component Analysis (DCA)
- Kernel Discriminative Component Analysis (KDCA)
- Global Distance Metric Learning by Convex Programming (GDMLCP)
Supervised Local Distance Metric Learning:
- Local Fisher Discriminant Analysis (LFDA)
- Kernel Local Fisher Discriminant Analysis (KLFDA)
- 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.
Contact authors of this package: