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jointDiag (version 0.4)

Joint Approximate Diagonalization of a Set of Square Matrices

Description

Different algorithms to perform approximate joint diagonalization of a finite set of square matrices. Depending on the algorithm, orthogonal or non-orthogonal diagonalizer is found. These algorithms are particularly useful in the context of blind source separation. Original publications of the algorithms can be found in Ziehe et al. (2004), Pham and Cardoso (2001) , Souloumiac (2009) , Vollgraff and Obermayer . An example of application in the context of Brain-Computer Interfaces EEG denoising can be found in Gouy-Pailler et al (2010) .

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install.packages('jointDiag')

Monthly Downloads

370

Version

0.4

License

GPL (>= 2)

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Maintainer

Cedric Gouy-Pailler

Last Published

October 27th, 2020

Functions in jointDiag (0.4)

jedi

Approximate non-orthogonal joint diagonalization of a set of square real-valued matrices
jadiag

Joint Approximate Diagonalization of a set of square, symmetric and real-valued matrices
ajd

Wrapper: Joint approximate diagonalization of a set of matrices
ffdiag

Joint Approximate Diagonalization of a set of square, symmetric and real-valued matrices
uwedge

Joint Approximate Diagonalization of a set of square, symmetric and real-valued matrices
qdiag

Joint Approximate Diagonalization of a set of square, symmetric and real-valued matrices