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. 2019 Jan-Feb;54(1):47-61.
doi: 10.1080/00273171.2018.1485006. Epub 2018 Nov 7.

Probabilistic Disjoint Principal Component Analysis

Carla Ferrara  1 , Francesca Martella  1 , Maurizio Vichi  1
Affiliations

Probabilistic Disjoint Principal Component Analysis

Carla Ferrara et al. Multivariate Behav Res. 2019 Jan-Feb.

Abstract

One of the most relevant problems in principal component analysis and factor analysis is the interpretation of the components/factors. In this paper, disjoint principal component analysis model is extended in a maximum-likelihood framework to allow for inference on the model parameters. A coordinate ascent algorithm is proposed to estimate the model parameters. The performance of the methodology is evaluated on simulated and real data sets.

Keywords: Probabilistic model; maximum-likelihood estimation; partition of variables.

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