Timeline for How to handle data imbalance in Principal Component Analysis?

8 events

when toggle format	what		by	license	comment
Jul 13, 2012 at 14:32	vote	accept	zca0
Jul 7, 2012 at 21:25	answer	added	Michael R. Chernick		timeline score: 0
Jul 7, 2012 at 10:43	comment	added	ttnphns		Is the essence of your question this: There's a multivariate data presumably heterogeneous due to presence of clusters (groups) in it; the clusters are not known, so discriminant analysis is not applicable. Is it then possible to use PCA in place of it, to differentiate the groups? If yes, how to make PCA maximally effective for this discriminative task? Etc. What I recommend you is to rework this and the other (just next) your questions and merge them into one question.
Jul 7, 2012 at 10:00	comment	added	zca0		Thanks. I have edited it. The question should be more clearly now.
Jul 7, 2012 at 9:59	history	edited	zca0	CC BY-SA 3.0	added 462 characters in body
Jul 7, 2012 at 9:38	history	edited	chl	CC BY-SA 3.0	deleted 3 characters in body; edited tags; edited title
Jul 7, 2012 at 9:28	comment	added	ttnphns		Let me ask you to clarify and detail your question. First, what is "imbalance"? Groups of very different n or else? So, do you have your sample split into groups or not? Second, PCA (?)`tries to keep the most discrimination power rather than keep most variations` seems to contradict with PCA `trying to keep most variations in data set`. Please, elucidate.
Jul 7, 2012 at 9:05	history	asked	zca0	CC BY-SA 3.0