At 04:23 AM 1/23/2004, Mike White wrote: >With predict.lda the posterior probabilities only relate to the existing >Class definitions. This is fine for Class definitions like gender but it is >a problem when new data does not necessarily belong to an existing Class. >Is there a classification method that gives posterior probabilities for >Class membership and does not assume the new data must belong to one of the >existing Classes? A new class would then be indicated by low posterior >probabilities for all the existing Classes.
For discriminant analysis, one of of doing this would be to use "typicality probabilities", sometimes called "inverse probabilities". This is discussed in Huberty's Applied Discriminant Analysis, Wiley 1994 (and other sources), and is used relatively commonly in the paleoanthropological literature where researchers try to "classify" a fossil into a matrix of modern forms to which it is already known that the fossil does not belong. The question is whether the fossil has a modern analog or whether it really is dissimilar from any modern form.
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