A2 Review article, Literature review, Systematic review
A review of second‐order blind identification methods (2022)
Pan, Y., Matilainen, M., Taskinen, S., & Nordhausen, K. (2022). A review of second‐order blind identification methods. WIREs Computational Statistics, 14(4), Article e1550. https://doi.org/10.1002/wics.1550
JYU authors or editors
Publication details
All authors or editors: Pan, Yan; Matilainen, Markus; Taskinen, Sara; Nordhausen, Klaus
Journal or series: WIREs Computational Statistics
ISSN: 1939-5108
eISSN: 1939-0068
Publication year: 2022
Publication date: 07/02/2021
Volume: 14
Issue number: 4
Article number: e1550
Publisher: John Wiley & Sons
Publication country: United States
Publication language: English
DOI: https://doi.org/10.1002/wics.1550
Publication open access: Openly available
Publication channel open access: Partially open access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/78633
Abstract
Second order source separation (SOS) is a data analysis tool which can be used for revealing hidden structures in multivariate time series data or as a tool for dimension reduction. Such methods are nowadays increasingly important as more and more high-dimensional multivariate time series data are measured in numerous fields of applied science. Dimension reduction is crucial, as modelling such high-dimensional data with multivariate time series models is often impractical as the number of parameters describing dependencies between the component time series is usually too high. SOS methods have their roots in the signal processing literature, where they were first used to separate source signals from an observed signal mixture. The SOS model assumes that the observed time series (signals) is a linear mixture of latent time series (sources) with uncorrelated components. The methods make use of the second order statistics - hence the name “second order source separation”. In this review we discuss the classical SOS methods and their extensions to more complex settings. An example illustrates how SOS can be performed.
Keywords: statistical methods; multivariable methods; time series; time-series analysis; signal processing; computational science
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2022
JUFO rating: 1