Least-Squares Joint Diagonalization of a matrix set by a congruence transformation

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dc.creatorCongedo, Marco
dc.creatorPham, Dinh-Tuan
dc.date2009-04-05
dc.date.accessioned2026-07-07T13:00:43Z
dc.date.available2026-07-07T13:00:43Z
dc.descriptionThe 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 the classical leastsquares criterion, a new normalization reveals a very simple form of the solution matrix. Numerical simulations shown that the algorithm, named SDIAG (spheric diagonalization), behaves well as compared to state-of-the art AJD algorithms.
dc.description2nd Singaporean-French IPAL Symposium, Singapour : Singapour (2009)
dc.identifierhttps://arxiv.org/abs/0904.0779
dc.identifierhttp://arxiv.org/abs/0904.0779
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/225924
dc.subjectComputation
dc.titleLeast-Squares Joint Diagonalization of a matrix set by a congruence transformation
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