Decorrelation of Neutral Vector Variables: Theory and Applications

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 mu...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2018-01, Vol.29 (1), p.129-143
Main Authors: Zhanyu Ma, Jing-Hao Xue, Leijon, Arne, Zheng-Hua Tan, Zhen Yang, Jun Guo
Format: Article
Language:English
Subjects:
Correlation analysis
Covariance matrices
Data processing
Decorrelation
Dirichlet problem
Dirichlet variable
Distributed databases
Genetic transformation
Image color analysis
Independent variables
Kernel
neutral vector
neutrality
non-Gaussian
Pattern recognition
Principal component analysis
Principal components analysis
Variables
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Summary: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.
ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2016.2616445