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Suppose that you have 1000 features, and a data set made up of say, 50,000 points. Suppose then that we perform PCA, and we extract the top 5 PCs, since they explain 99.99 percent of the variance, and thats all we care about.
From those top 5 PCs, can we 'go backwards', and be able to decipher, what the most 'important' features were from the original 1000? For example, can we answers the question, "What combination of my original 1000 features were responsible for my top PC?"