rSFA (version 1.00)

sfaClassify: Predict Class for SFA classification

Description

Create a SFA classification mode, predict & evaluate on new data (xtst,realc_tst). Author of orig. matlab version: Wolfgang Konen, May 2009 - Jan 2010 See also [Berkes05] Pietro Berkes: Pattern recognition with Slow Feature Analysis. Cognitive Sciences EPrint Archive (CogPrint) 4104, http://cogprints.org/4104/ (2005)

Usage

sfaClassify(x, realclass, xtst = 0, realcTst = 0, opts)

Arguments

x

NREC x IDIM, training input data

realclass

1 x NREC, training class labels

xtst

NTST x IDIM, test input data

realcTst

1 x NTST, test class labels

opts

list with several parameter settings:

Value

• list res - res$errtrn 1 x 2 matrix: error rate with / w/o SFA on training set - res$errtst 1 x 2 matrix: error rate with / w/o SFA on test set - res$y output from SFA when applied to training data - res$ytst output from SFA when applied to test data - res$predT predictions with SFA + GaussClassifier on test set - res$predX predictions w/o SFA (only GaussClassifier) on test set