pekalska: Pekalska's approach to speeding up Sammon's mapping.

In mp: Multidimensional Projection Techniques

Description

Creates a k-dimensional representation of the data. As input, a subsample and its k-dimensional mapping are required. The method approximates the subsample mapping to a linear mapping based on the distances matrix of the subsample and then applies the same mapping to all instances.

Usage

pekalska(D, sample.indices = NULL, Ys = NULL)

Arguments

D	dist object or distances matrix.
sample.indices	The indices of subsamples.
Ys	The subsample mapping (k-dimensional).

Value

The low-dimensional representation of the data.

References

Pekalska, E., de Ridder, D., Duin, R. P., & Kraaijveld, M. A. (1999). A new method of generalizing Sammon mapping with application to algorithm speed-up (pp. 221-228).

mp documentation built on May 1, 2019, 10:19 p.m.