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For regression problems with #Predictors > #observations, I recently read about Moore Penrose (pseudo inverse method) which solves the problem of non invertible matrix in OLS for regression problems.

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How well does this 'generally' perform as compared to some of the other alternatives such as Ridge/Lasso, Partial least squares, Principle component regression?

Also, while datasets with predictors > observations, is there a method among the above listed that always performs better than the rest (purely in terms of prediction accuracy)?

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