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I have sets of features of different nature(for example, 300 features from FFT-transform, 1000 categorical features and so on). However there are only 900 samples and I`m trying to select important features using Lasso. So the question is: should I perform feature selection differentially on subsets of features or better firstly, concatenate features and only then perform feature selection?

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Let LASSO pick the best ones. If the features are highly correlated and you want them picked as a group, add some L2 regularization too. This is called Elastic Net regularization, and it is a generalization of L1 and L2 regularization. Other than that, do not feel obliged to artificially group features.

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