vect: Vectorization of covariance matrices

In LauraMasiero/RiemannR: RiemannR - EEG-based BCI analysis with Riemannian geometry

Description

This function vectorizes sample covariance matrices with the formulation presented by

Usage

vect(x)

Arguments

x	a numeric matrix.

Details

This type of vectorization takes the upper triangular part of a matrix, multiplying the non-diagonal elements with an appropriate weight. For more details see References.

Value

It returns the vectorized form of the input matrix.

Author(s)

Laura Masiero, email: [e-mail]

References

Barachant, Alexandre, Stéphane Bonnet, Marco Congedo e Christian Jutten (2013). "Classification of covariance matrices using a Riemannian-based kernel for BCI applications". In: Neurocomputing. issn: 09252312. doi: 10.1016/j.neucom.2012.12.039.

LauraMasiero/RiemannR documentation built on Sept. 29, 2020, 9:51 p.m.