Laplacian_Eigenmaps: Laplacian Eigenmap

In kcf-jackson/maniTools: Manifold learning in R

Description

Laplacian Eigenmap

Usage

Laplacian_Eigenmaps(X, k, d)

Arguments

X	N x D matrix (N samples, D features).
k	integer; Number of nearest neighbor.
d	integer; The target dimension.

Details

Matlab codes were written by Belkin & Niyogi (2001) and extracted from Todd Wittman's MANI: Manifold Learning Toolkit.

Value

a list of two objects. The first is the projected data (which are eigenvectors) and the second is the corresponding eigenvalues.

References

Belkin, M., & Niyogi, P. (2001, December). Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. In NIPS (Vol. 14, pp. 585-591).

MANI: Manifold Learning Toolkit - http://macs.citadel.edu/wittman/research.html

Examples

#Simulate data
sim_data <- swiss_roll(N = 600)
library(plotly)
p1 <- plotly_3D(sim_data); p1
LE_data <- Laplacian_Eigenmaps(sim_data$data, k = 8, d = 2)
p2 <- plotly_2D(LE_data$eigenvectors, color = sim_data$colors); p2

kcf-jackson/maniTools documentation built on April 20, 2020, 8:29 a.m.