Distance Metric Learning through the Maximization of the Jeffrey divergence (DMLMJ).

Source: R/dml.R

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.

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.