SpectralMap: Diffusion Map and Spectral Map

In SpectralMap: Diffusion Map and Spectral Map

Description

Implements the diffusion map method of dimensionality reduction and spectral method of combining multiple diffusion maps, including creation of the spectra and visualization of maps.

Usage

SpectralMap(data, epsilon=0.1, range=1, Plot2D=FALSE, Plot3D=FALSE)

Arguments

data	a list of datasets and each column in each dataset is a variable
epsilon	parameter in the Gaussian kernel
range	indexes of the datasets in the data list to be combined and computed. If length(range)==1, only diffusion map will be computed. Otherwise, spectral map will be computed
Plot2D	a logical value indicating whether a 2-D map should be plotted
Plot3D	a logical value indicating whether a 3-D map should be plotted

Value

singularvector

the spectra of either diffusion map or spectral map

Examples

#generate two datasets
n <- 100
theta <- 2*pi*seq(from=0, to=1-1/n, by=1/n)
r = (1 + cos(6*theta)/4)

# X is a circle
X1 = cos(theta)
X2 = sin(theta)
X<-data.frame(X1,X2)

#Y is a hexagon
Y1 = r*cos(theta)
Y2 = r*sin(theta)
Y<-data.frame(Y1,Y2)

#create data list
Data<-list(X,Y)

#create the diffusion map of X
example1<-SpectralMap(Data, epsilon=0.1, range=1, Plot2D=TRUE, Plot3D=FALSE)
#create the spectral map from X to Y
example2<-SpectralMap(Data, epsilon=0.1, range=1:2, Plot2D=TRUE, Plot3D=FALSE)

Example output

SpectralMap documentation built on May 2, 2019, 2:40 a.m.