pyDML
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Current Algorithms:
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Average Neighborhood Margin Maximization (ANMM)
Local Linear Discriminant Analysis (LLDA)
Large Margin Nearest Neighbors (LMNN)
Neighborhood Component Analysis (NCA)
Nearest Class Mean Metric Learning (NCMML)
Nearest Class with Multiple Centroids (NCMC)
Information Theoretic Metric Learning (ITML)
Distance Metric Learning through the Maximization of the Jeffrey Divergence (DMLMJ)
Maximally Collapsing Metric Learning (MCML)
Learning with Side Information (LSI)
Distance Metric Learning with Eigenvalue Optimization (DML-eig)
Logistic Discriminant Metric Learning (LDML)
Kernel Large Margin Nearest Neighbors (KLMNN)
Kernel Average Neighborhood Margin Maximization (KANMM)
Kernel Distance Metric Learning through the Maximization of the Jeffrey divergence (KDMLMJ)
Kernel Discriminant Analysis (KDA)
Kernel Local Linear Discriminant Analysis (KLLDA)
Additional functionalities
Distance metric learning extensions for some Scikit-Learn classifiers
Distance metric and classifier plots
Tuning parameters
Overview
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