Theis, Fabian J. and Inouye, Y. (2006) On the use of joint diagonalization in blind signal processing. In: Proceedings / IEEE International Symposium on Circuits and Systems, ISCAS 2006, May 21 - 24, 2006 ,Kos, Greece. IEEE Service Center, Piscataway, NJ. ISBN 0-7803-9389-9.
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Blind source separation (BSS) tries to decompose a given multivariate data set into the product of a mixing matrix and a source vector, both of which are unknown. The sources can be recovered if we pose additional constraints to this model. One class of BSS algorithms is given by algebraic BSS, which recovers the mixing structure by jointly diagonalizing various source condition matrices corresponding to different source models. We review classical BSS algorithms such as FOBI, JADE, AMUSE, SOBI, TDSEP and SONS within this framework; combination of the respective source conditions can then yield additional algorithms as implemented e.g. by JADETD. Extensions to dependent component analysis models such as spatiotemporal or multidimensional BSS are discussed.
|Item Type:||Book Section|
|Institutions:||Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang|
|Subjects:||500 Science > 570 Life sciences|
|Created at the University of Regensburg:||Unknown|
|Deposited On:||12 Oct 2010 11:36|
|Last Modified:||12 Oct 2010 11:36|
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