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Theis, F.J. ; Kawanabe, M.* ; Müller, K.-R.*

Uniqueness of non-Gaussianity-based dimension reduction.

IEEE Trans. Signal Process. 59, 4478 - 4482 (2011)
Verlagsversion DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Dimension reduction is a key step in preprocessing large-scale data sets. A recently proposed method named non-Gaussian component analysis searches for a projection onto the non-Gaussian part of a given multivariate recording, which is a generalization of the deflationary projection pursuit model. In this contribution, we discuss the uniqueness of the subspaces of such a projection. We prove that a necessary and sufficient condition for uniqueness is that the non-Gaussian signal subspace is of minimal dimension. Furthermore, we propose a measure for estimating this minimal dimension and illustrate it by numerical simulations. Our result guarantees that projection algorithms uniquely recover the underlying lower dimensional data signals.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Identifiability; Independent subspace analysis; non-Gaussian component analysis; projection pursuit
ISSN (print) / ISBN 1053-587X
e-ISSN 1941-0476
Quellenangaben Band: 59, Heft: 9, Seiten: 4478 - 4482 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort Piscataway, NJ
Begutachtungsstatus Peer reviewed