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Non-orthogonal joint diagonalization (NJD) free of prewhitening has been widely studied in the context of blind source separation (BSS) and array signal processing, etc. However, NJD is used to retrieve the jointly diagonalizable structure for a single set of target matrices which are mostly formulized with a single dataset, and thus is insufficient to(More)
Joint diagonalization (JD) is an instrumental tool in a vast variety of applications such as blind source separation, polarization sensitive array processing, and linear algebra based computation of tensor decompositions. Among the JD families, those based on successive rotations are a major category that minimizes the adopted highly nonlinear cost function(More)
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