# riem.isomap: Isometric Feature Mapping In Riemann: Learning with Data on Riemannian Manifolds

## Description

ISOMAP - isometric feature mapping - is a dimensionality reduction method to apply classical multidimensional scaling to the geodesic distance that is computed on a weighted nearest neighborhood graph. Nearest neighbor is defined by k-NN where two observations are said to be connected when they are mutually included in each other's nearest neighbor. Note that it is possible for geodesic distances to be Inf when nearest neighbor graph construction incurs separate connected components. When an extra parameter padding=TRUE, infinite distances are replaced by 2 times the maximal finite geodesic distance.

## Usage

 1 2 3 4 5 6 7 riem.isomap( riemobj, ndim = 2, nnbd = 5, geometry = c("intrinsic", "extrinsic"), ... ) 

## Arguments

 riemobj a S3 "riemdata" class for N manifold-valued data. ndim an integer-valued target dimension (default: 2). nnbd the size of nearest neighborhood (default: 5). geometry (case-insensitive) name of geometry; either geodesic ("intrinsic") or embedded ("extrinsic") geometry. ... extra parameters including paddinga logical; if TRUE, Inf-valued geodesic distances are replaced by 2 times the maximal geodesic distance in the data.

## Value

a named list containing

embed

an (N\times ndim) matrix whose rows are embedded observations.

## References

\insertRef

silva_global_2003Rdimtools

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 #------------------------------------------------------------------- # Example on Sphere : a dataset with three types # # 10 perturbed data points near (1,0,0) on S^2 in R^3 # 10 perturbed data points near (0,1,0) on S^2 in R^3 # 10 perturbed data points near (0,0,1) on S^2 in R^3 #------------------------------------------------------------------- ## GENERATE DATA mydata = list() for (i in 1:10){ tgt = c(1, stats::rnorm(2, sd=0.1)) mydata[[i]] = tgt/sqrt(sum(tgt^2)) } for (i in 11:20){ tgt = c(rnorm(1,sd=0.1),1,rnorm(1,sd=0.1)) mydata[[i]] = tgt/sqrt(sum(tgt^2)) } for (i in 21:30){ tgt = c(stats::rnorm(2, sd=0.1), 1) mydata[[i]] = tgt/sqrt(sum(tgt^2)) } myriem = wrap.sphere(mydata) mylabs = rep(c(1,2,3), each=10) ## MDS AND ISOMAP WITH DIFFERENT NEIGHBORHOOD SIZE mdss = riem.mds(myriem)$embed iso1 = riem.isomap(myriem, nnbd=5)$embed iso2 = riem.isomap(myriem, nnbd=10)\$embed ## VISUALIZE opar = par(no.readonly=TRUE) par(mfrow=c(1,3), pty="s") plot(mdss, col=mylabs, pch=19, main="MDS") plot(iso1, col=mylabs, pch=19, main="ISOMAP:nnbd=5") plot(iso2, col=mylabs, pch=19, main="ISOMAP:nnbd=10") par(opar) 

Riemann documentation built on June 20, 2021, 5:07 p.m.