mvmds: Multiview MDS on a list of matrices or 'dist' objects.

In dpmccabe/multiview: Multiview Clustering and Dimensionality Reduction Methods

Description

Multiview multidimensional scaling (mvmds) receives two or more feature matrices or dist objects (or any combination of both) and produces a low-dimensional representation of the samples according to the combined information in all the input data.

Usage

mvmds(x, k = 2)

Arguments

x	A list of data matrices or dist objects. Both types can be mixed. In the case of plain data matrices, euclidean distance will be used to generate a dist object for that data view.
k	Number of desired dimensions of the low-dimensional projection.

Value

A n x k matrix with the k-dimensional projection, where n is the number of samples in the dataset.

Note

All input views must have the same number of samples (rows).

Examples

x1 <- iris[, 1:2]
x2 <- iris[, 3:4]
mvmds(list(x1, x2), k = 2)

dpmccabe/multiview documentation built on May 5, 2019, 12:30 p.m.