pyriemann.utils.ajd.uwedge¶
- pyriemann.utils.ajd.uwedge(X, *, init=None, eps=1e-07, n_iter_max=100)¶
Approximate joint diagonalization based on UWEDGE.
Implementation of the AJD algorithm by Tichavsky and Yeredor [1] [2]: uniformly weighted exhaustive diagonalization using Gauss iterations (U-WEDGE). This is a translation from the matlab code provided by the authors.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of symmetric matrices to diagonalize.
- initNone | ndarray, shape (n_channels, n_channels), default=None
Initialization for the diagonalizer.
- epsfloat, default=1e-7
Tolerance for stoping criterion.
- n_iter_maxint, default=100
The maximum number of iterations to reach convergence.
- Returns:
- Vndarray, shape (n_channels, n_channels)
The diagonalizer.
- Dndarray, shape (n_matrices, n_channels, n_channels)
Set of quasi diagonal matrices.
Notes
New in version 0.2.4.
References
[1]A Fast Approximate Joint Diagonalization Algorithm Using a Criterion with a Block Diagonal Weight Matrix P. Tichavsky, A. Yeredor and J. Nielsen. 2008 IEEE International Conference on Acoustics, Speech and Signal ProcessingICASSP.
[2]Fast Approximate Joint Diagonalization Incorporating Weight Matrices P. Tichavsky and A. Yeredor. IEEE Transactions on Signal Processing, Volume 57, Issue 3, March 2009.