Kernel Wiener Filter with Distance Constraint

In this paper, we introduce a non-iterative nonlinear kernel Wiener filtering method using kernel canonical correlation analysis (CCA) framework. This approach is based upon the theory of reproducing kernel Hubert spaces. A method is proposed to find approximate Wiener filtered signal in the original signal space by solving an optimization problem in the higher dimensional space. Unlike the conventional iterative approaches which rely on nonlinear optimization problem, our proposed method directly finds the pre-image using distance constraints in the higher mapped domain. The signal estimation and reconstruction capability of the new method is demonstrated and benchmarked on the United States Postal Service (USPS) digits database. Moreover, a comparison with the conventional kernel Wiener filter is presented