KANMM: The kernelized version of ANMM.

In jlsuarezdiaz/rDML: Distance Metric Learning Algorithms for R

Description

The kernelized version of ANMM.

Usage

KANMM(num_dims = NULL, n_friends = 3, n_enemies = 1,
  kernel = "linear", gamma = NULL, degree = 3, coef0 = 1,
  kernel_params = NULL)

Arguments

num_dims	Dimension desired for the transformed data. Integer. If NULL, all features will be taken. Integer.
n_friends	Number of nearest same-class neighbors to compute homogeneus neighborhood. Integer.
n_enemies	Number of nearest different-class neighbors to compute heterogeneus neigborhood. Integer.
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 KANMM transformer, structured as a named list.

References

Fei Wang and Changshui Zhang. “Feature extraction by maximizing the average neighborhood margin”. In: Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on. IEEE. 2007, pages 1-8.

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