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Kernel-based adaptive filters present a new opportunity to re-cast nonlinear optimization problems over a Reproducing Kernel Hilbert Space (RKHS), transforming the nonlinear task to linear, where easier and well-known methods may be used. The approach can be seen to yield solutions suitable for sparse adaptive filtering. The new Complex Kernel Least Mean Square algorithm (CKLMS), derived by Bouboulis...
Nonlinear adaptive filters are getting more common and are useful especially where performance of linear adaptive filters may be unacceptable. Such areas include communications, image processing and biological systems. Quaternion valued data has also been drawing recent interest in various areas of statistical signal processing, including adaptive filtering, image pattern recognition, and modeling...
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