L2PCA_approx {pcaL1} | R Documentation |
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.