id
	16361
Url
	https://chloe.cnr.it/s/BiDiAr/item/16361
Resource template
	Academic Article
Resource class
	bibo:AcademicArticle
Title
	A Guide for Sparse PCA: Model Comparison and Applications
Creator
	Guerra-Urzola, Rosember
	Van Deun, Katrijn
	Vera, Juan C.
	Sijtsma, Klaas
Date
	2021
Language
	eng
Abstract
	PCA is a popular tool for exploring and summarizing multivariate data, especially those consisting of many variables. PCA, however, is often not simple to interpret, as the components are a linear combination of the variables. To address this issue, numerous methods have been proposed to sparsify the nonzero coefficients in the components, including rotation-thresholding methods and, more recently, PCA methods subject to sparsity inducing penalties or constraints. Here, we offer guidelines on how to choose among the different sparse PCA methods. Current literature misses clear guidance on the properties and performance of the different sparse PCA methods, often relying on the misconception that the equivalence of the formulations for ordinary PCA also holds for sparse PCA. To guide potential users of sparse PCA methods, we first discuss several popular sparse PCA methods in terms of where the sparseness is imposed on the loadings or on the weights, assumed model, and optimization criterion used to impose sparseness. Second, using an extensive simulation study, we assess each of these methods by means of performance measures such as squared relative error, misidentification rate, and percentage of explained variance for several data generating models and conditions for the population model. Finally, two examples using empirical data are considered.
Is Part Of
	Psychometrika
Doi
	https://doi.org/10.1007/s11336-021-09773-2
Issn
	1860-0980
Issue
	4
Pages
	893-919
Short title
	A Guide for Sparse PCA
Volume
	86
Homepage
	https://www.zotero.org/groups/5293298/bidiar/items/A6SGSQRQitem-list

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