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:
Supervised Local Distance Metric Learning:
The algorithms and routines might be adjusted during developing.
Contact authors of this package:
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