Preprocessing of high-dimensional features is a very general and powerful method for improving the performance of a learning algorithm. The preprocessing need not be linear but can be a general (nonlinear) function of the form x'=g(x). The derived features x' can then be used as inputs into any (linear or nonlinear) learning procedure.
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