TY - JOUR
T1 - Decorrelation of Neutral Vector Variables
T2 - Theory and Applications
AU - Ma, Zhanyu
AU - Xue, Jing-Hao
AU - Leijon, Arne
AU - Tan, Zheng-Hua
AU - Yang, Zhen
AU - Guo, Jun
PY - 2018/1
Y1 - 2018/1
N2 - 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.
AB - 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.
KW - Decorrelation
KW - Dirichlet variable
KW - neutral vector
KW - neutrality
KW - non-Gaussian
UR - http://www.scopus.com/inward/record.url?scp=84995370852&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2016.2616445
DO - 10.1109/TNNLS.2016.2616445
M3 - Journal article
SN - 2162-237X
VL - 29
SP - 129
EP - 143
JO - I E E E Transactions on Neural Networks and Learning Systems
JF - I E E E Transactions on Neural Networks and Learning Systems
IS - 1
M1 - 7676372
ER -