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Decorrelation of Neutral Vector Variables: Theory and Applications
KTH, School of Electrical Engineering (EES).
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2018 (English)In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 29, no 1, p. 129-143Article in journal (Refereed) Published
Abstract [en]

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two fundamental invertible transformations, namely, serial nonlinear transformation and parallel nonlinear transformation, are proposed to carry out the decorrelation. For a neutral vector variable, which is not multivariate-Gaussian distributed, the conventional principal component analysis cannot yield mutually independent scalar variables. With the two proposed transformations, a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom. We also evaluate the decorrelation performances for the vectors generated from a single Dirichlet distribution and a mixture of Dirichlet distributions. The mutual independence is verified with the distance correlation measurement. The advantages of the proposed decorrelation strategies are intensively studied and demonstrated with synthesized data and practical application evaluations.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2018. Vol. 29, no 1, p. 129-143
Keywords [en]
Decorrelation, Dirichlet variable, neutral vector, neutrality, non-Gaussian
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-221860DOI: 10.1109/TNNLS.2016.2616445ISI: 000419558900011PubMedID: 27834653Scopus ID: 2-s2.0-84995370852OAI: oai:DiVA.org:kth-221860DiVA, id: diva2:1179062
Note

QC 20180131

Available from: 2018-01-31 Created: 2018-01-31 Last updated: 2018-04-04Bibliographically approved

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Leijon, Arne

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