A guide for sparse PCA: Model comparison and applications

Rosember Guerra Urzola*, Katrijn Van Deun, J. C. Vera, K. Sijtsma

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)893–919
JournalPsychometrika
Volume86
DOIs
Publication statusPublished - 2021

Fingerprint

Dive into the research topics of 'A guide for sparse PCA: Model comparison and applications'. Together they form a unique fingerprint.

Cite this