ArticlePsychometrika2021
A Guide for Sparse PCA: Model Comparison and Applications.
Article in Psychometrika, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Empirical Bayes Covariance Decomposition, and a Solution to the Multiple Tuning Problem in Sparse PCA.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2026Article
- Exploratory structural equation modeling and the curse of dimensionality.Behavior research methods · 2026Article
- Structured dissociative PCA methods for high dimensional neuroimaging signal decomposition.Scientific reports · 2026Article
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- Exploring non-target screening variability in unsupervised multivariate time trend analysis of LC-HRMS data.Analytical and bioanalytical chemistry · 2026Article
- Translatability of Animal Models for Alzheimer's Disease Using a Machine Learning Based Workflow.Clinical and translational science · 2025Article
- Comprehensive review of dimensionality reduction algorithms: challenges, limitations, and innovative solutions.PeerJ. Computer science · 2025Article
- Comparative evaluation of feature reduction methods for drug response prediction.Scientific reports · 2024Article
- Semisynthetic simulation for microbiome data analysis.Briefings in bioinformatics · 2024Review
- Logistic regression with sparse common and distinctive covariates.Behavior research methods · 2023Article
- Simultaneous clustering and variable selection: A novel algorithm and model selection procedure.Behavior research methods · 2023Article
- Volatile Markers for Cancer in Exhaled Breath-Could They Be the Signature of the Gut Microbiota?Molecules (Basel, Switzerland) · 2023Article
- Topological data analysis expands the genotype to phenotype map for 3D maize root system architecture.Frontiers in plant science · 2023Article
- Cellular miR-6741-5p as a Prognostic Biomarker Predicting Length of Hospital Stay among COVID-19 Patients.Viruses · 2022Article
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Authors and funding
4 authors.
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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.
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