mspca: Multiple Sparse PCA

In msPCA: Sparse Principal Component Analysis with Multiple Principal Components

Multiple Sparse PCA

Description

Returns multiple sparse principal component of a matrix using an iterative deflation heuristic.

Usage

mspca(
  Sigma,
  r,
  ks,
  maxIter = 200L,
  verbose = TRUE,
  feasibilityConstraintType = 0L,
  feasibilityTolerance = 1e-04,
  stallingTolerance = 1e-08,
  maxIterTPM = 20L,
  timeLimitTPM = 20L,
  restartsAfterFirstIter = 10L
)

Arguments

Sigma	A matrix. The correlation or covariance matrix, whose sparse PCs will be computed.
r	An integer. Number of principal components (PCs) to be computed.
ks	A list of integers. Target sparsity of each PC.
maxIter	(optional) An integer. Maximum number of iterations of the algorithm. Default 200.
verbose	(optional) A Boolean. Controls console output. Default TRUE.
feasibilityConstraintType	(optional) An integer. Type of feasibility constraints to be enforced. 0: orthogonality constraints; 1: uncorrelatedness constraints. Default 0.
feasibilityTolerance	(optional) A float. Tolerance for the violation of the orthogonality constraints. Default 1e-4
stallingTolerance	(optional) A float. Controls the objective improvement below which the algorithm is considered to have stalled. Default 1e-8
maxIterTPM	(optional) An integer. Maximum number of random restarts of the truncated power method (inner iteration) for the first outer iteration. Default 20.
timeLimitTPM	(optional) An integer. Maximum time in seconds for the truncated power method (inner iteration). Default 20.
restartsAfterFirstIter	(optional) An integer. Maximum number of random restarts of the truncated power method (inner iteration) for outer iterations >= 2. Default 10.

Value

An object with 4 fields: 'x_best' (p x r array containing the sparse PCs), 'objective_value', 'feasibility_violation', 'runtime'.

Examples

library(datasets)
TestMat <- cor(datasets::mtcars)
mspca(TestMat, 2, c(4,4))

msPCA documentation built on May 16, 2026, 1:08 a.m.