Estimates the m-mode covariance matrix from an array of array-valued observations.
Array of order higher than two with the last dimension corresponding to the sampling units.
The mode with respect to which the covariance matrix is to be computed.
Logical, indicating whether the observations should be centered prior to computing the covariance matrix. Default is
The m-mode covariance matrix provides a higher order analogy for the ordinary covariance matrix of a random vector and is computed for a random tensor X of size p_1 x p_2 x ... x p_r as Cov_m(X) = E(X(m) X(m)^T)/(p_1 ... p_(m-1) p_(m+1) ... p_r), where X(m) is the centered m-flattening of X. The algorithm computes the estimate of this based on the sample
m-mode covariance matrix of
x having the size p_m x p_m.
Virta, J., Li, B., Nordhausen, K. and Oja, H., (2017), Independent component analysis for tensor-valued data, Journal of Multivariate Analysis, doi: 10.1016/j.jmva.2017.09.008
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