DMLMJ: Distance Metric Learning through the Maximization of the...

In jlsuarezdiaz/rDML: Distance Metric Learning Algorithms for R

Description

A DML Algorithm that obtains a transformer that maximizes the Jeffrey divergence between the distribution of differences of same-class neighbors and the distribution of differences between different-class neighbors.

Usage

DMLMJ(num_dims = NULL, n_neighbors = 3, alpha = 0.001,
  reg_tol = 1e-10)

Arguments

num_dims	Dimension desired for the transformed data. If NULL, dimension will be the number of features.
n_neighbors	Number of neighbors to consider in the computation of the difference spaces.
alpha	Regularization parameter for inverse matrix computation.
reg_tol	Tolerance threshold for applying regularization. The tolerance is compared with the matrix determinant.

Value

The DMLMJ transformer, structured as a named list.

References

Bac Nguyen, Carlos Morell and Bernard De Baets. “Supervised distance metric learning through maximization of the Jeffrey divergence”. In: Pattern Recognition 64 (2017), pages 215-225.

jlsuarezdiaz/rDML documentation built on May 24, 2019, 12:35 a.m.