Journal of Computational and Graphical Statistics · 2000 · 38 citations · 16 references
ComputingData ScienceData MiningData VisualizationFeature EngineeringData AnalysisData InterpretationDimension ReductionDimensionality ReductionMultivariate AnalysisPrincipal Component AnalysisCovariance Matrix DecompositionExplained VariancePrincipal ComponentsVariance Maximization
Abstract It is fairly common, following a principal component analysis, to rotate components in order to simplify their structure. Here, we propose an alternative to this two-stage procedure which involves only one stage and combines the objectives of variance maximization and simplification. It is shown, using examples, that the new technique can provide alternative ways of interpreting a dataset. Some properties of the technique are investigated using a simulation study.
16
Methods of multivariate analysis
Choice Reviews Online · 1995 · 2.3K citations
Rotation of principal components
Michael B. Richman · Journal of Climatology · 1986 · 1.8K citations
Peter J. Huber · The Annals of Statistics · 1985 · 1.2K citations