A1 Journal article (refereed)
On the usage of joint diagonalization in multivariate statistics (2022)


Nordhausen, K., & Ruiz-Gazen, A. (2022). On the usage of joint diagonalization in multivariate statistics. Journal of Multivariate Analysis, 188, Article 104844. https://doi.org/10.1016/j.jmva.2021.104844


JYU authors or editors


Publication details

All authors or editorsNordhausen, Klaus; Ruiz-Gazen, Anne

Journal or seriesJournal of Multivariate Analysis

ISSN0047-259X

eISSN1095-7243

Publication year2022

Volume188

Article number104844

PublisherElsevier

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1016/j.jmva.2021.104844

Publication open accessOpenly available

Publication channel open accessPartially open access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/79049


Abstract

Scatter matrices generalize the covariance matrix and are useful in many multivariate data analysis methods, including well-known principal component analysis (PCA), which is based on the diagonalization of the covariance matrix. The simultaneous diagonalization of two or more scatter matrices goes beyond PCA and is used more and more often. In this paper, we offer an overview of many methods that are based on a joint diagonalization. These methods range from the unsupervised context with invariant coordinate selection and blind source separation, which includes independent component analysis, to the supervised context with discriminant analysis and sliced inverse regression. They also encompass methods that handle dependent data such as time series or spatial data.


Keywordsmathematical statisticsmultivariable methodsindependent component analysis

Free keywordsBlind source separation; Dimension reduction; Invariant component selection; Scatter matrices; Supervised dimension reduction


Contributing organizations


Ministry reportingYes

Reporting Year2022

JUFO rating1


Last updated on 2024-26-03 at 09:20