A New View on Regularization Theory

This paper provides a new viewpoint on regularization theory from different perspectives. It is shown that the regularized solution can be derived from the Fourier transformation operator in transformation domain with equivalent form from linear differential operator in spatial domain. The state-of-the-art research in regularization are briefly reviewed with extended discussions on Occam's razor, minimum length description, Bayesian framework, pruning algorithms, statistical learning theory, and equivalent regularization.