pyriemann.utils.mean.mean_ale

pyriemann.utils.mean.mean_ale(X=None, tol=1e-06, maxiter=50, sample_weight=None, covmats=None)

AJD-based log-Euclidean (ALE) mean of SPD matrices.

Return the mean of a set of SPD matrices using the approximate joint diagonalization (AJD) based log-Euclidean (ALE) mean [1].

Parameters:

Xndarray, shape (n_matrices, n, n)

Set of SPD matrices.

tolfloat, default=10e-7

The tolerance to stop the gradient descent.

maxiterint, default=50

The maximum number of iterations.

sample_weightNone | ndarray, shape (n_matrices,), default=None

Weights for each matrix. If None, it uses equal weights.

Returns:

Mndarray, shape (n, n)

ALE mean.

See also

mean_covariance

Notes

New in version 0.2.4.

References

[1]

Approximate Joint Diagonalization and Geometric Mean of Symmetric Positive Definite Matrices M. Congedo, B. Afsari, A. Barachant, M. Moakher. PLOS ONE, 2015