get_voronoi_feature: The function to get feature representation in RKHS based on...

In MaxWiK: Machine Learning Method Based on Isolation Kernel Mean Embedding

The function to get feature representation in RKHS based on Voronoi diagram for WHOLE dataset

Description

The function to get feature representation in RKHS based on Voronoi diagram for WHOLE dataset

Usage

get_voronoi_feature(
  psi = 40,
  t = 350,
  data,
  talkative = FALSE,
  Matrix_Voronoi = NULL
)

add_new_point_iKernel(data, d1, Matrix_Voronoi, dissim, t, psi, nr)

Arguments

psi	Integer number related to the size of each Voronoi diagram
t	Integer number of trees in Isolation Kernel or dimension of RKHS
data	dataset of points, rows - points, columns - dimensions of a point
talkative	logical. If TRUE then print messages, FALSE for the silent execution
Matrix_Voronoi	Matrix of Voronoi diagrams, if it is NULL then the function will calculate Matrix_Voronoi
d1	Data point - usually it is an observation data point
dissim	Matrix of dissimilarity or distances between all points.
nr	Integer number of rows in matrix of distances (dissim) and also the size of dataset

Value

Feature representation in RKHS based on Voronoi diagram for WHOLE dataset

RKHS mapping for a new point based on Isolation Kernel mapping

Functions

• add_new_point_iKernel(): The function to get RKHS mapping based on Isolation Kernel for a new point

Examples

NULL
NULL

MaxWiK documentation built on April 3, 2025, 8:47 p.m.