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Approximate joint diagonalization within the Riemannian geometry framework.

, , , and . EUSIPCO, page 210-214. IEEE, (2016)

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Approximate Joint Diagonalization According to the Natural Riemannian Distance., , and . LVA/ICA, volume 10169 of Lecture Notes in Computer Science, page 290-299. (2017)Random Matrix Improved Covariance Estimation for a Large Class of Metrics., , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 6254-6263. PMLR, (2019)Mining the bilinear structure of data with approximate joint diagonalization., , , and . EUSIPCO, page 667-671. IEEE, (2016)A closed-form unsupervised geometry-aware dimensionality reduction method in the Riemannian Manifold of SPD matrices., , , , and . EMBC, page 3198-3201. IEEE, (2017)Approximate joint diagonalization within the Riemannian geometry framework., , , and . EUSIPCO, page 210-214. IEEE, (2016)Géométrie et optimisation riemannienne pour la diagonalisation conjointe : application à la séparation de sources d'électroencéphalogrammes. (Riemannian geometry and optimization for approximate joint diagonalization : application to source separation of electroencephalograms).. Grenoble Alpes University, France, (2018)Intrinsic Cramér-Rao Bounds for Scatter and Shape Matrices Estimation in CES Distributions., , , and . IEEE Signal Process. Lett., 26 (2): 262-266 (2019)Dimensionality Reduction for BCI Classification using Riemannian Geometry., , , and . GBCIC, Verlag der Technischen Universitaet Graz, (2017)