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While using PCA for feature dimension reduction,is it required to convert the reduced featureset to original axes?

问题描述:

I know i have asked similar question, but still not sure i have got appropriate reply.I have separate train and test data sets. The feature size is high , 400. I performed PCA on train and test data to reduce feature dimension before classification using SVM. My doubt is do i need to convert the reduced size features with Principle Components as axes to original axes using command such as

OriginalData=RowFeatureVector' * FinalTransformedData + Mean

where RowFeatureVector' isthe matrix of eigenvectors as columns and FinalTransformedData is the transformed data and Mean is the vector of means.

OR i can procced with classification using reduced features in principal component axes?

Help for the same will be highly appreciated as my work is held up.

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