similarity_hica: Estimate of the similarity matrix
In fastHICA: Hierarchical Independent Component Analysis: a Multi-Scale Sparse Non-Orthogonal Data-Driven Basis

Description Usage Arguments Details Value Note Author(s) References See Also

This function provides an estimate of the similarity matrix of the original data, before performing HICA algorithm.

1	similarity_hica(X, dim.subset = 512)

`X`	Data matrix with nrow(X) observations and ncol(X) variables.
`dim.subset`	The dimension of the subset used for the evaluation of the similarity index (i.e., distance correlation). If this it is greater than nrow(X) all the observations are used, unless a random subset of `dim.subset` observations is used. The default value is set to 512.

This function is auxiliary for the basis_hica function. Indeed its output is the estimate of the similarity matrix at the first step of the algorithm.

`similarity_matrix`	similarity matrix of the original data.
`subset`	subset used for the evaluation of distance correlation between variables.

The distance correlation is evaluated through the function dcor of the package "energy". It becomes computationally unfeasible if the number of observations is too large. For this reason it is possibile to choose the dimension of the subsample to be used in the evaluation of the similarity matrix. By default the dimension is set to 512.

Piercesare Secchi, Simone Vantini, and Paolo Zanini.

P. Secchi, S. Vantini, and P. Zanini (2014). Hierarchical Independent Component Analysis: a multi-resolution non-orthogonal data-driven basis. MOX-report 01/2014, Politecnico di Milano.

basis_hica, energy_hica, extract_hica

fastHICA documentation built on May 2, 2019, 9:25 a.m.