| Paper: | SPTM-P11.9 | ||
| Session: | Nonlinear Systems and Signal Processing | ||
| Time: | Friday, May 19, 10:00 - 12:00 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Nonlinear Systems and Signal Processing | ||
| Title: | A CLOSED FORM SOLUTION FOR A NONLINEAR WIENER FILTER | ||
| Authors: | Puskal Pokharel, Jian-Wu Xu, University of Florida, United States; Deniz Erdogmus, Oregon Health & Science University, United States; Jose Principe, University of Florida, United States | ||
| Abstract: | In this paper a nonlinear extension to the Wiener filter is presented. A direct approach has been devised of replacing the autocorrelation function with a novel function called correntropy, derived from ideas on kernel-based learning theory and information theoretic learning. The linear Wiener filter, widely used because of its simplicity and optimality for linear systems and Gaussian distribution, is no longer effective when dealing with nonlinear time series data. The proposed method incorporates higher order moments in the general form of autocorrelation and improves upon the linear filter. Moreover, the computation cost is still lower than some kernel based methods and has a closed form solution to the problem unlike neural network based methods. | ||
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