mvsc: Multiview spectral clustering on a list of matrices or 'dist'...

In dpmccabe/multiview: Multiview Clustering and Dimensionality Reduction Methods

Description

Computes the multiview spectral clustering of data on a list of matrices or dist objects (or a mix of both), supposed to be different views of the same data.

Usage

mvsc(x, k, sigmas = NULL, neighbours = NULL, clustering = TRUE)

Arguments

x	A list of feature matrices or dist objects (or a mix of both).
k	Number of desired clusters.
sigmas	Either NULL, a single real value or a vector of real values. They correspond to the sigma parameter in the Gaussian radial basis function. If it is NULL then the default sigma computation is used (average distance to the log(n)-th neighbour, with n = number of samples), unless neighbours has a value different from NULL. If it is a single number then the same sigma is applied to all input views. If it is a vector each value in it is applied to the corresponding input view.
neighbours	Either NULL, a single integer value or a vector of integers. They correspond to the expected number of neighbours per point, used to estimate the sigma values of the Gaussian radial basis function. If it is NULL then the default sigma computation is used (average distance to the log(n)-th neighbour, with n = number of samples). If it is a single value then the same number of neighbours is used on all input views, else each value in the vector is applied to the corresponding input view. Does not have effect if sigma is different from NULL.
clustering	Tells mvsc if it has to perform the clustering on the projection (if TRUE) or to skip the clustering step of the algorithm.

Value

A list with four elements: clustering is a vector of integers with the clustering assignment of each sample (not included if clustering = FALSE), evalues is a matrix with the eigenvalues of the common principal components (CPC) step, evectors is a matrix with the eigenvectors of the CPC step, and sigmas is a vector with the sigmas used on the Gaussian radial basis function of each input view.

Note

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

Examples

m1 <- iris[, 1:2]
m2 <- iris[, 3:4]
mvsc(list(m1, m2), k = 3)

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