A2 Katsausartikkeli tieteellisessä aikausilehdessä
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-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Pan, Yan; Matilainen, Markus; Taskinen, Sara; Nordhausen, Klaus
Lehti tai sarja: WIREs Computational Statistics
ISSN: 1939-5108
eISSN: 1939-0068
Julkaisuvuosi: 2022
Ilmestymispäivä: 07.02.2021
Volyymi: 14
Lehden numero: 4
Artikkelinumero: e1550
Kustantaja: John Wiley & Sons
Julkaisumaa: Yhdysvallat (USA)
Julkaisun kieli: englanti
DOI: https://doi.org/10.1002/wics.1550
Julkaisun avoin saatavuus: Avoimesti saatavilla
Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/78633
Tiivistelmä
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.
YSO-asiasanat: tilastomenetelmät; monimuuttujamenetelmät; aikasarjat; aikasarja-analyysi; signaalinkäsittely; laskennallinen tiede
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2022
Alustava JUFO-taso: 1
