L2PCA_approx: L2PCA_approx

In pcaL1: L1-Norm PCA Methods

L2PCA_approx

Description

Provides an approximation of traditional PCA described by Park and Klabjan (2016) as a subroutine for awl1pca.

Usage

L2PCA_approx(ev.prev, pc.prev, projDim, X.diff)

Arguments

ev.prev	matrix of principal component loadings from a previous iteration of awl1pca
pc.prev	vector of eigenvalues from previous iteration of awl1pca
projDim	number of dimensions to project data into, must be an integer
X.diff	The difference between the current weighted matrix estimate and the estimate from the previous iteration

Details

The calculation is performed according to equations (11) and (12) in Park and Klabjan (2016). The method is an approximation for traditional principal component analysis.

Value

'L2PCA_approx' returns a list containing the following components:

eigenvalues	Estimate of eigenvalues of the covariance matrix.
eigenvectors	Estimate of eigenvectors of the covariance matrix.

References

Park, Y.W. and Klabjan, D. (2016) Iteratively Reweighted Least Squares Algorithms for L1-Norm Principal Component Analysis, IEEE International Conference on Data Mining (ICDM), 2016.

See Also

awl1pca

pcaL1 documentation built on Jan. 22, 2023, 1:55 a.m.