Hessian_LLE: Hessian Local Linear Embedding
In kcf-jackson/maniTools: Manifold learning in R

Description Usage Arguments Details Value References Examples

Hessian Local Linear Embedding

1	Hessian_LLE(X, k, d)

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

Matlab codes were written by David Donoho & Carrie Grimes (2003) and extracted from Todd Wittman's MANI: Manifold Learning Toolkit.

A list of two objects. The first is the projected data, the second is the mse.

Donoho, D. L., & Grimes, C. (2003). Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proceedings of the National Academy of Sciences, 100(10), 5591-5596.

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

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