FHCSkernelEVD: Carries out the kernelEVD algorithm for data reduction

In FastHCS: Robust Algorithm for Principal Component Analysis

Description

This step reduces the data space to the affine subspace spanned by the n observations.

Usage

  FHCSkernelEVD(x,best=NULL,q=NULL)

Arguments

x	A data matrix.
best	An optional subset of 1:n.
q	Desired rank of the SVD decomposition. Optional.

Value

A reduced data set with full rank.

Author(s)

Small modification of the code from the classPC from rrcov.

References

Wu, W., Massart, D. L., and de Jong, S. (1997), 'The Kernel PCA Algorithms for Wide Data. Part I: Theory and Algorithms,' Chemometrics and Intelligent Laboratory Systems,36,165–172

Examples

n<-50
p<-200
x<-matrix(rnorm(n*p),nc=p)
W<-FHCSkernelEVD(x)

Example output

Loading required package: matrixStats
Loading required package: robustbase

Attaching package: 'robustbase'

The following objects are masked from 'package:matrixStats':

    colMedians, rowMedians

FastHCS documentation built on July 8, 2020, 7:14 p.m.