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This question already has an answer here:

I know that Principal Component Analysis (PCA) is the eigenvector of the covariance matrix. It is used as a tool for dimensional reduction. What I am confused about is whether the PCA give weights to original features in order to find out which features explain the data the most or does it come up with new set of abstract features that explain the greatest variance in the data set.

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marked as duplicate by amoeba, Richard Hardy, Greenparker, ttnphns, Silverfish Aug 19 '16 at 11:51

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  • $\begingroup$ Please check the existing threads. There are some truly excellent answers (e.g. by @amoeba) to almost everything you need to know about PCA. $\endgroup$ – Richard Hardy Aug 19 '16 at 9:38
  • $\begingroup$ @amoeba I did go through your post but I am not convinced if that answers my question. $\endgroup$ – thethakuri Aug 19 '16 at 10:22
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    $\begingroup$ It looks to me that the thread over there and the question posed here really amount to the same thing. Perhaps you should edit this question to make it more clear, what aspect of the other thread was unclear to you? (I'm guessing that you might want a bit more about the "weights to original feature" aspect, but that's only a supposition - as the question is currently phrased it is hard to see that it can be given an answer that would not also fit at the proposed duplicate.) $\endgroup$ – Silverfish Aug 19 '16 at 12:48
  • $\begingroup$ It's not only "my post", thethakuri; there are 26 answers in that thread and many of them are very good. If they do not answer your question that's fine, but please make sure to look through that thread and then edit your question to make it clear what aspect you do not understand, as @Silverfish suggested above. $\endgroup$ – amoeba Aug 19 '16 at 13:14
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    $\begingroup$ Okay. That answers my question. $\endgroup$ – thethakuri Aug 21 '16 at 9:26

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