KDMLMJ: The kernelized version of DMLMJ.
In jlsuarezdiaz/rDML: Distance Metric Learning Algorithms for R

Description Usage Arguments Value References

The kernelized version of DMLMJ.

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KDMLMJ(num_dims = NULL, n_neighbors = 3, alpha = 0.001,
  reg_tol = 1e-10, kernel = "linear", gamma = NULL, degree = 3,
  coef0 = 1, kernel_params = NULL)

`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.
`kernel`	Kernel to use. Allowed values are: "linear" \| "poly" \| "rbf" \| "sigmoid" \| "cosine" \| "precomputed".
`gamma`	Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels. Default value is 1/n_features. Float.
`degree`	Degree for poly kernels. Ignored by other kernels. Integer.
`coef0`	Independent term for poly and sigmoid kernels. Ignored by other kernels. Float.
`kernel_params`	Parameters (keyword arguments) and values for kernel passed as callable object. Ignored by other kernels.

The KDMLMJ transformer, structured as a named list.

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.