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Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.

that considering the number of "positive cases" that I have, I have too many predictors (I have about 450 cases in total, about 100 positive cases, and more than 20 predictors.) I'd like to use PCA … for about 75% of the respondents and 99+ for a few. I understand that there's nothing conceptually wrong with using PCA with such variables. But PCA finds the directions of maximum variance in data …
asked Feb 2 '13 by user765195
variables were skewed, I tried two alternative paths: Before doing the PCA, I used a logarithmic transformation to reduce the skew in variables. I used Mia Hubert's ROBPCA algorithm, as implemented by the … can I use robust principal components, instead of trying to make my data look like normal? Are there any particular robust PCA methods that you'd recommend instead of ROBPCA? …
asked Aug 2 '12 by user765195
I'm not sure if I agree with your statement about better interpretation for PCA when applied to iid variables. Normality helps because for normal rv's, variance is the natural measure of dispersion … , but the iid condition isn't really necessary. When you don't have normal random variables, you can try Hubert et al's Robust PCA for skewed data: Hubert et al.: "Robust PCA for skewed data and its outlier map" …
answered Sep 19 '12 by user765195