Multivariate Pattern Classification

SPM 2010 - 13. Kurs zur funktionellen BildgebungClassificationSPM-Kurs Hamburg 2010Situation 1: scans , features¡(i.ei.e. whole brain data)FLD unsuitable: depends on reliable estimation of covariance matrixGNB inferior to SVM and LR the latter come with regularisationthat help weigh down the effects of noisy and highly correlatedfeaturesCox & Savoy (2003). NeuroImageClassificationSPM-Kurs Hamburg 2010Situation 2: scans¢, features¢(i.ei.e. feature selection orfeature extraction) GNB, SVM and LR: often similar performance SVM originally designed for two-classproblems only SVM for multiclass problems: : multiple binarycomparisons, voting scheme to identify classes accuracy of SVM increases faster than GNB when thenumber of scans increase see Mitchell et al. (2005) for further comparisonsbetween different classifiersClassificationSPM-Kurs Hamburg 2010Peeking #2 classifier performance = unbiased estimate ofclassification accuracy how well would the classifier label a new examplerandomly drawn from the same distribution? testing a trained classifier needs to be performed on adataset the classifier has never seen before if entire dataset is used for training a classifier,classification estimates become overly optimisticSolution: leave-oneone-outout crossvalidationPattern Classification12