| lmds {lmds} | R Documentation |
Landmark MDS
Description
A fast dimensionality reduction method scaleable to large numbers of samples. Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical 'Torgerson MDS', but rather than calculating a complete distance matrix between all pairs of samples, only the distances between a set of landmarks and the samples are calculated.
A fast dimensionality reduction method scaleable to large numbers of samples. Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS´, but rather than calculating a complete distance matrix between all pairs of samples, only the distances between a set of landmarks and the samples are calculated.
Usage
lmds(x, ndim = 3, distance_method = c("euclidean", "pearson",
"spearman", "cosine", "manhattan"), landmark_method = c("sample"),
num_landmarks = 500)
Arguments
x |
A matrix, optionally sparse. |
ndim |
The number of dimensions |
distance_method |
The distance metric to use. Options are "euclidean" (default), "pearson", "spearman", "cosine", "manhattan". |
landmark_method |
The landmark selection method to use. Options are "sample" (default). |
num_landmarks |
The number of landmarks to use, |
Value
The dimensionality reduction in the form of a nrow(x) by ndim matrix.
Examples
library(Matrix)
x <- Matrix::rsparsematrix(1000, 1000, .01)
lmds(x, ndim = 3)