Since lattice algebra based associative memories can store any number k of associated vector pairs (x, y), where x is a real n-dimensional vector and y is a real m-dimensional vector, we propose a basic redundancy mechanism to endow with retrieval capability the dual canonical min-W and max-M lattice associative memories for inputs corrupted by random noise. To achieve our goal, given a finite set of exemplar vectors, redundant patterns are added in order to enlarge the original fixed point set of the original exemplars. The redundant patterns are masked versions designed to be spatially correlated with each exemplar x in a given learning data set. An illustrative example with noisy color images are given to measure the retrieval capability performance of the proposed redundancy technique as considered for lattice associative memories.
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