首页|On the usage of joint diagonalization in multivariate statistics

On the usage of joint diagonalization in multivariate statistics

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  • NSTL
  • Elsevier
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. (C) 2021 The Author(s). Published by Elsevier Inc.

Blind source separationDimension reductionIndependent component analysisInvariant component selectionScatter matricesSupervised dimension reductionBLIND SOURCE SEPARATIONINDEPENDENT COMPONENT ANALYSISSLICED INVERSE REGRESSIONDIMENSION REDUCTIONOUTLIER DETECTIONSCATTERIDENTIFICATIONALGORITHMLOCATIONMATRICES

Ruiz-Gazen, Anne、Nordhausen, Klaus

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Univ Toulouse Capitole

Univ Jyvaskyla

2022

10.1016/j.jmva.2021.104844

Journal of Multivariate Analysis

Journal of Multivariate Analysis

SCI
ISSN：0047-259X
年,卷(期)：2022.188
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