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dml (Distance Metric Learning in R)

Brief Intro

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:

The algorithms and routines might be adjusted during developing.


Track Devel:

Report Bugs:


Contact the maintainer of this package: Yuan Tang

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dml documentation built on May 2, 2019, 6:35 a.m.