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I have two questions:

1-Which method is appropriate for dimensionality reduction for feature extraction when missing some feature values?

2-Which textbook is the best source for the answer in (1)?

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  • $\begingroup$ I am of the opinion that most dimension reduction techniques cannot handle missing features $\endgroup$ – Jayaram Iyer May 1 at 11:26
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Denoising autoencoders will be your bet in this case. I don't have a book handy for this case. They are good at reconstruction and calculate a good latent representation. Just replace your missing feature with the mean or a fixed value.

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You can look for probabilistic PCA, I heard somewhere that it can perform well with missing data.

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