The sdml package aims to implement the state-of-the-art algorithms for supervised distance metric learning. It includes global and local methods such as (Kernel) Relevant Component Analysis, (Kernel) Discriminative Component Analysis, (Kernel) Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.
|Author||Tao Gao <[email protected]>, Nan Xiao <[email protected]>, Yuan Tang <[email protected]>|
|Maintainer||Yuan Tang <[email protected]>|
|License||MIT + file LICENSE|
|Package repository||View on GitHub|
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