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Results tagged with Search options user 5264

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.

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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
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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
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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