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

(Last Updated On: December 14, 2011)
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Quant analytics: how to check that Data reduction is correct after applying PCA to a data set



! 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!



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About caustic

Hi i there My name is Bryan Downing. I am part of a company called QuantLabs.Net This is specifically a company with a high profile blog about technology, trading, financial, investment, quant, etc. It posts things on how to do job interviews with large companies like Morgan Stanley, Bloomberg, Citibank, and IBM. It also posts different unique tips and tricks on Java, C++, or C programming. It posts about different techniques in learning about Matlab and building models or strategies. There is a lot here if you are into venturing into the financial world like quant or technical analysis. It also discusses the future generation of trading and programming Specialties: C++, Java, C#, Matlab, quant, models, strategies, technical analysis, linux, windows P.S. I have been known to be the worst typist. Do not be offended by it as I like to bang stuff out and put priorty of what I do over typing. Maybe one day I can get a full time copy editor to help out. Do note I prefer videos as they are much easier to produce so check out my many video at youtube.com/quantlabs