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Quant analytics: how to check that Data reduction is correct after applying PCA to a data set

(Last Updated On: December 14, 2011)

Quant analytics: how to check that Data reduction is correct after applying PCA to a data set

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! You should check that the Cumulative Proportion of Variance of the number of dimensions you decide to take is enough (about 80%: it depends on the field). On R, you clearly see that with the “summary” command, where you see the proportion of variance due to each component. In a few words, you can reduce data if you do not lose too much information: so if you decide to take the two principal components, their cumulative proportion of variance should be enough in order to well represent the original data set. Of course the cumulative proportion reaches 100% only if you take all the dimensions, but very often only a couple of them are necessary to explain a big part of the original data. Hope this helps!

 

 

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

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