Open AccessDynamically sampled multivariate empirical mode decomposition
Author(s) -
Rehman N.,
Naveed K.,
Safdar M.W.,
Ehsan S.,
McDonaldMaier K.D.
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2015.1176
Subject(s) - multivariate statistics , curvature , hilbert–huang transform , projection (relational algebra) , mathematics , mode (computer interface) , random variate , measure (data warehouse) , signal (programming language) , algorithm , decomposition , computer science , statistics , random variable , data mining , geometry , white noise , operating system , ecology , biology , programming language
A method for accurate multivariate local mean estimation in the multivariate empirical mode decomposition algorithm by using a statistical data‐driven approach based on the Menger curvature measure and normal‐to‐anything variate‐generation method is proposed. This is achieved by aligning the projection vectors in the direction of the maximum ‘activity’ of the input signal by considering the local curvature of the signal in multidimensional spaces, resulting in accurate mean estimation even for a very small number of projection vectors.