KDA: Kernel Discriminant Analysis (KDA).

In jlsuarezdiaz/rDML: Distance Metric Learning Algorithms for R

Description

Discriminant analysis in high dimensionality using the kernel trick.

Usage

KDA(solver = "eigen", n_components = NULL, tol = 1e-04,
  kernel = "linear", gamma = NULL, degree = 3, coef0 = 1,
  kernel_params = NULL)

Arguments

solver	Solver to use, posible values: - 'eigen': Eigenvalue decomposition.
n_components	Number of components (lower than number of classes -1) for dimensionality reduction. If NULL, classes - 1 is used. Integer.
tol	Singularity toleration level. Float.
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.

Value

The KDA transformer, structured as a named list.

References

Sebastian Mika et al. “Fisher discriminant analysis with kernels”. In: Neural networks for signal processing IX, 1999. Proceedings of the 1999 IEEE signal processing society workshop. Ieee. 1999, pages 41-48.

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