Randomly generated nonlinear transformations for pattern recognition

A general method is proposed to find non-linear transformations for discrete data in information processing problems. The main feature of the method is random perturbation of the data subject to constraints which ensure that in the transformed space the problem is in some sense simpler. The technique has been applied to pattern recognition by finding a nonlinear transformation of the feature space such that all classes become linearly separable.