est.clustering {Rdimtools} | R Documentation |
Intrinsic Dimension Estimation via Clustering
Description
Instead of directly using neighborhood information, est.clustering
adopts hierarchical
neighborhood information using hclust
by recursively merging leafs
over the range of radii.
Usage
est.clustering(X, kmin = round(sqrt(nrow(X))))
Arguments
X |
an |
kmin |
minimal number of neighborhood size to search over. |
Value
a named list containing containing
- estdim
estimated intrinsic dimension.
Author(s)
Kisung You
References
Eriksson B, Crovella M (2012). “Estimating Intrinsic Dimension via Clustering.” In 2012 IEEE Statistical Signal Processing Workshop (SSP), 760–763.
Examples
## create 'swiss' roll dataset
X = aux.gensamples(dname="swiss")
## try different k values
out1 = est.clustering(X, kmin=5)
out2 = est.clustering(X, kmin=25)
out3 = est.clustering(X, kmin=50)
## print the results
line1 = paste0("* est.clustering : kmin=5 gives ",round(out1$estdim,2))
line2 = paste0("* est.clustering : kmin=25 gives ",round(out2$estdim,2))
line3 = paste0("* est.clustering : kmin=50 gives ",round(out3$estdim,2))
cat(paste0(line1,"\n",line2,"\n",line3))
[Package Rdimtools version 1.1.2 Index]