Beyond standard classes of generalized joint signal representations of arbitrary variables: Mercer kernel-based representations

We present an approach for extending the scope of standard covariant signal representations by means of implicit nonlinear mappings applied to signals via Mercer kernels. One of the advantages of using such kernels is that we do not need to exhibit the underlying nonlinear maps to be able to compute signal representations. This gives increased computational efficiency. Finally, conditions on kernels to preserve covariance properties are finally discussed.