In PCA, we often think of the principal components / eigenvectors as a linear combination of the original features in vector space. Is the reverse true as well, i.e., is each feature a linear combination of the principal components?

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    $\begingroup$ Do you mean exactly or approximately? $\endgroup$ – whuber Jun 29 at 21:54
  • $\begingroup$ @whuber Hmm, I originally meant theoretically, so exactly. But your question makes me think that the answer is "no" to exactly, but that it can be approximately written as a linear combination of the PCs, so I would be interested in that answer as well. $\endgroup$ – David Jun 29 at 22:00
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    $\begingroup$ The answer depends on how many PCs you retain. The whole idea behind PCA is to approximate the original features using a smaller number of PCs. By taking all the PCs, nothing is lost. Have you looked at our higher voted threads on PCA? $\endgroup$ – whuber Jun 29 at 22:05
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    $\begingroup$ @whuber Yup. It's actually this stats.stackexchange.com/questions/183236/… one that inspired my question. The answer by amoeba says "The intuition is that PCA seeks to represent all $n$ data vectors as linear combinations of a small number of eigenvectors, and does it to minimize the mean-squared reconstruction error." I've never seen it phrased this way before, but it seems to be saying the features are represented as a linear combination of the PCs. $\endgroup$ – David Jun 29 at 22:14
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    $\begingroup$ That's right. That's the basis of just about every plot of PCs that has ever been produced: the data are approximated as points in a space spanned by two (or sometimes three) PCs. $\endgroup$ – whuber Jun 29 at 22:40

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