pyDML
stable

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

  • Package documentation - Indices and tables
  • dml
  • Applications
  • Examples
  • Installation
  • Stats
  • References
pyDML
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© Copyright 2018, Juan Luis Suárez Díaz Revision d42e75db.

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