8/12/2019 09_analise_componentes

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The goals:

Select the optimum number lof features

Select the bestlfeatures

Large lhas a three-fold disadvantage:

High computational demands

Low generalization performance

Poor error estimates

FEATURE SELECTION

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GivenN

lmust be large enough to learn

what makes classes different

what makes patterns in the same class similar

l must be small enough not to learn what makespatterns of the same class different

In practice, has been reported to be a sensiblechoice for a number of cases

Once l has been decided, choose the l most informativefeatures

Best: Large between class distance,Small within class variance

3/l