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LEAST-SQUARES JOINT DIAGONALIZATION OF A MATRIX SET BY A CONGRUENCE TRANSFORMATION
Proceedings of the Singaporean-French Ipal Symposium 2009
The approximate joint diagonalization (AJD) is an important analytic tool at the base of numerous independent component analysis (ICA) and other blind source separation (BSS) methods, thus finding more and more applications in medical imaging analysis. In this work we present a new AJD algorithm named SDIAG (Spheric Diagonalization). It imposes no constraint either on the input matrices or on the joint diagonalizer to be estimated, thus it is very general. Whereas it is well grounded on thedoi:10.1142/9789814277563_0010 fatcat:bymy55nwszfrth2zar5tcrxlny