A Guide for Sparse PCA: Model Comparison and Applications.

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 potenti

SUBMITTER: Guerra-Urzola R 

PROVIDER: S-EPMC8636462 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature