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sdml

Note: This package has been moved to a new package, which will be maintained actively.

Introduction

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

Algorithms planned in the first development stage:

  • Supervised Global Distance Metric Learning:

    • Relevant Component Analysis (RCA)
    • Kernel Relevant Component Analysis (KRCA)
    • Discriminative Component Analysis (DCA)
    • Kernel Discriminative Component Analysis (KDCA)
    • Global Distance Metric Learning by Convex Programming (GDMLCP)
  • Supervised Local Distance Metric Learning:

    • Local Fisher Discriminant Analysis (LFDA)
    • Kernel Local Fisher Discriminant Analysis (KLFDA)
    • Information-Theoretic Metric Learning (ITML)
    • Large Margin Nearest Neighbor Classifier (LMNN)
    • Neighbourhood Components Analysis (NCA)
    • Localized Distance Metric Learning (LDM)

The algorithms and routines might be adjusted during developing.

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Supervised Distance Metric Learning with R

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