I have telecom data with large number of dimentions. Now if I apply dimentionality reduction like PCA then from resulting dimention say PC1, PC2 I would loose the meaning or would not understand what they represent.

Are their any techniques other than PCA which can provide any meaning or intution about the new dimentions. Also suggest if there are any research papers in this.

  • $\begingroup$ Can you provide the PCA output sample for interpretation ? You need to provide a Loading plot too so can be understood the positive or negative association in order to provide the explanation of PCA in context if your data. $\endgroup$ – n1tk Sep 27 '19 at 20:49

To give meaning to the Axis of the PCA, you can study the scalar product between the two new axes and all your original axes (you have to normalise the vectors before doing that).

Those axes with a high scalar product will be highly associated the with the new axes and viceversa. This may help you interpret them.

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  • $\begingroup$ Could you guide me the steps or share some resources to exactly do that...thanks for the info......also do you suggest any better method than PCA since my data has lot of categorical data $\endgroup$ – shrish123 kumar Sep 26 '19 at 2:44

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