2019
Sparse principal component analysis with missing observations
Park S, Zhao H. Sparse principal component analysis with missing observations. The Annals Of Applied Statistics 2019, 13: 1016-1042. DOI: 10.1214/18-aoas1220.Peer-Reviewed Original ResearchHigh-dimensional settingsPrincipal subspaceStep estimation procedureRate of convergenceSparse principal component analysisDimensional settingSimulated examplesMissing observationsStatistical methodsEstimation procedureSparse PCA methodsSingle-cell dataSubspacePCA methodSingle-cell RNA-sequencing dataNumber of featuresCompetitive performancePrincipal component analysisConvergenceSample sizeEstimationWide rangeComponent analysis
2012
Sparse principal component analysis by choice of norm
Qi X, Luo R, Zhao H. Sparse principal component analysis by choice of norm. Journal Of Multivariate Analysis 2012, 114: 127-160. PMID: 23524453, PMCID: PMC3601508, DOI: 10.1016/j.jmva.2012.07.004.Peer-Reviewed Original ResearchHigh-dimensional situationsSparse principal component analysisReal gene expression dataEfficient iterative algorithmHigh-dimensional dataSparse principal component analysis methodEigenvalue problemOptimization problemIterative methodChoice of normDimensional situationTheoretical resultsTraditional eigenvalue problemIterative algorithmStrict convexityLinear combinationSingle-component modelExpensive computationSparse linear combinationDimensional dataUsual normExistence of correlationsGene expression dataPractical applicationsCompetitive results