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Every time you compress the feature space you are losing some information. The original feature engineering stage you outlined sounds like a meaningful compression & might make sense in the context of your problem. The second compression on the other hand might only serve to lose some information. I would only perform the second compression if the ...


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Welcome to the site. PCA is an unsupervised dimensionality reduction algorithm. It works by transforming the original feature-set into eigen-vectors that are difficult to map with the original feature set. As such, the first Principal Component (PC) contains the features with maximum variance. The subsequent PCs contain features with decreased variance to ...


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Very interesting question! I didn't get if the out put of one input vector is just one image or a set of images but this does not change the solution much. I propose you directly learn the relation between input and the variance of output images. For simplicity, I assume you target the pixle-wise variance within an output image. Then your training data would ...


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