Dataset Information

Procrustes Analysis for High-Dimensional Data.

ABSTRACT: The Procrustes-based perturbation model (Goodall in J R Stat Soc Ser B Methodol 53(2):285-321, 1991) allows minimization of the Frobenius distance between matrices by similarity transformation. However, it suffers from non-identifiability, critical interpretation of the transformed matrices, and inapplicability in high-dimensional data. We provide an extension of the perturbation model focused on the high-dimensional data framework, called the ProMises (Procrustes von Mises-Fisher) model. The ill-posed and interpretability problems are solved by imposing a proper prior distribution for the orthogonal matrix parameter (i.e., the von Mises-Fisher distribution) which is a conjugate prior, resulting in a fast estimation process. Furthermore, we present the Efficient ProMises model for the high-dimensional framework, useful in neuroimaging, where the problem has much more than three dimensions. We found a great improvement in functional magnetic resonance imaging connectivity analysis because the ProMises model permits incorporation of topological brain information in the alignment's estimation process.

SUBMITTER: Andreella A

PROVIDER: S-EPMC9636303 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Procrustes Analysis for High-Dimensional Data.

Andreella Angela A Finos Livio L

Psychometrika 20220518 4

The Procrustes-based perturbation model (Goodall in J R Stat Soc Ser B Methodol 53(2):285-321, 1991) allows minimization of the Frobenius distance between matrices by similarity transformation. However, it suffers from non-identifiability, critical interpretation of the transformed matrices, and inapplicability in high-dimensional data. We provide an extension of the perturbation model focused on the high-dimensional data framework, called the ProMises (Procrustes von Mises-Fisher) model. The il ...[more]

PMID: 35583747

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Dataset Information

Procrustes Analysis for High-Dimensional Data.

Publications

Procrustes Analysis for High-Dimensional Data.

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