tpm: Truncated Power Method

In msPCA: Sparse Principal Component Analysis with Multiple Principal Components

Truncated Power Method

Description

Returns the leading sparse principal component of a matrix using the truncated power method.

Usage

tpm(Sigma, k, maxIter = 200L, verbose = TRUE, timeLimit = 10L)

Arguments

Sigma	A matrix. The correlation or covariance matrix, whose sparse PCs will be computed.
k	An integer. Target sparsity of the PC.
maxIter	(optional) An integer. Maximum number of iterations of the algorithm. Default 200.
verbose	(optional) A Boolean. Controls console output. Default TRUE.
timeLimit	(optional) An integer. Maximum time in seconds. Default 10.

Value

An object with 3 fields: 'x_best' (p x 1 array containing the sparse PC), 'objective_value', 'runtime'.

References

Yuan, X. T., & Zhang, T. (2013). Truncated power method for sparse eigenvalue problems. The Journal of Machine Learning Research, 14(1), 899-925.

Examples

library(datasets)
TestMat <- cor(datasets::mtcars)
tpm(TestMat, 4)

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