A review of second‐order blind identification methods

• Published in: Wiley interdisciplinary reviews. Computational statistics Vol. 14; no. 4; pp. e1550 - n/a
• Main Authors: Pan, Yan, Matilainen, Markus, Taskinen, Sara, Nordhausen, Klaus
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.07.2022
• Subjects: blind source separation; dimension reduction; joint diagonalization; multivariate time series; joint diagonalization; multivariate time series; blind source separation; dimension reduction
• ISSN: 1939-5108; 1939-0068
• DOI: 10.1002/wics.1550

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 modeling 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. This article is categorized under: Statistical Models > Time Series Models Statistical and Graphical Methods of Data Analysis > Dimension Reduction Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Second‐order source separation, which is a variant of blind source separation (BSS), recovers unobservable latent times series from their observed mixtures.