estimate_mle1: Maximum Likelihood Esimation with Poisson Process

In Rdimtools: Dimension Reduction and Estimation Methods

Maximum Likelihood Esimation with Poisson Process

Description

Assuming the density in a hypersphere is constant, authors proposed to build a likelihood structure based on modeling local spread of information via Poisson Process. est.mle1 requires two parameters that model the reasonable range of neighborhood size to reflect inhomogeneity of distribution across data points.

Usage

est.mle1(X, k1 = 10, k2 = 20)

Arguments

X	an (n\times p) matrix or data frame whose rows are observations.
k1	minimum neighborhood size, larger than 1.
k2	maximum neighborhood size, smaller than n.

Value

a named list containing containing

estdim

estimated intrinsic dimension.

Author(s)

Kisung You

References

\insertRef

levina_maximum_2005Rdimtools

Examples

## create example data sets with intrinsic dimension 2
X1 = aux.gensamples(dname="swiss")
X2 = aux.gensamples(dname="ribbon")
X3 = aux.gensamples(dname="saddle")

## acquire an estimate for intrinsic dimension
out1 = est.mle1(X1)
out2 = est.mle1(X2)
out3 = est.mle1(X3)

## print the estimates
line1 = paste0("* est.mle1 : 'swiss'  estiamte is ",round(out1$estdim,2))
line2 = paste0("* est.mle1 : 'ribbon' estiamte is ",round(out2$estdim,2))
line3 = paste0("* est.mle1 : 'saddle' estiamte is ",round(out3$estdim,2))
cat(paste0(line1,"\n",line2,"\n",line3))

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.