Using mutual information to measure the predictive power of principal components

Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090 2021. Using mutual information to measure the predictive power of principal components. Bura, Efstathia and Li, Bing, eds. Festschrift in Honor of R. Dennis Cook, Springer, pp. 1-16. (10.1007/978-3-030-69009-0_1)

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Abstract

In this work we propose the use of mutual information to measure the predictive potential of principal components in regression. We show that this criterion produces the same results as previous works which used the correlation to measure the strength of the relationship between the response variable with the extracted principal components in Gaussian settings. We demonstrate this in the linear regression model and also beyond that, in the conditional mean model and the conditional independence model, two common choices in sufficient dimension reduction, achieving a connection between unsupervised and supervised dimension reduction methods.

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Schools > Mathematics
Subjects:	Q Science > QA Mathematics
Publisher:	Springer
ISBN:	9783030690083
Last Modified:	21 Aug 2025 14:23
URI:	https://orca.cardiff.ac.uk/id/eprint/136830

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