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Combination of an autoassociative morphological memory and the kernel method

We recently introduced a class of highly nonlinear associative memories called morphological associative memories (MAMs). Notable features of autoassociative morphological memories (AMMs) include optimal absolute storage capacity and one-step convergence. The fixed points can be characterized exactly in terms of the original patterns. Unfortunately, AMM fixed points include a large number of spurious memories. In this paper, we use a combination of a basic AMM model and the kernel method in order to eliminate most of the spurious memories while leaving other AMM properties intact. Furthermore, our new AMM model is more tolerant to noise than a basic AMM model and less dependent on kernel selection than the original kernel method.