Man pages for SPCAvRP
Sparse Principal Component Analysis via Random Projections (SPCAvRP)

final_estimatorComputes the leading eigenvector from its support
project_covarianceProjects the sample covariance
select_projectionSelects the best projection
select_projections_subspaceSelects the best projections for the subspace estimation
SPCAvRPComputes the leading eigenvector using the SPCAvRP algorithm
SPCAvRP_deflationComputes the leading eigenvectors using the modified...
SPCAvRP_parallelParallel implementation of the SPCAvRP algorithm
SPCAvRP_rankingRanks the variables
SPCAvRP_subspaceComputes the leading eigenspace using the SPCAvRP algorithm...
SPCAvRP documentation built on Jan. 20, 2018, 9:06 a.m.