sfaPBootstrap: Parametric Bootstrap

In rSFA: Slow Feature Analysis

Parametric Bootstrap

Description

If training set too small, augment it with parametric bootstrap

Usage

sfaPBootstrap(realclass, x, sfaList)

Arguments

realclass	true class of training data (can be vector, numerics, integers, factors)
x	matrix containing the training data
sfaList	list with several parameter settings, e.g. as created by sfa2Create sfaList$xpDimFun (=xpDim by default) calculated dimension of expaned SFA space sfaList$deg degree of expansion (should not be 1, not implemented) sfaList$ppRange ppRange for SFA algorithm sfaList$nclass number of unique classes sfaList$doPB do (1) or do no (0) param. bootstrap.

Value

a list list containing:

x	training set extended to minimu number of recors1.5*(xpdim+nclass), if necessary
realclass	training class labels, extended analogously

See Also

addNoisyCopies

rSFA documentation built on March 29, 2022, 5:05 p.m.