Kernel methods and their applications to signal processing

Recently introduced in machine learning, the notion of kernels has drawn a lot of interest as it allows nonlinear algorithms to be obtained from linear ones in a simple and elegant manner. This, in conjunction with the introduction of new linear classification methods such as the support vector machines has produced significant progress. The success of such algorithms is now spreading as they are applied to more and more domains. Many signal processing problems, by their nonlinear and high-dimensional nature, may benefit from such techniques. We give an overview of kernel methods and their recent applications.