Efficient serial associative memory

Probabilistic algorithms are presented for efficient storage and retrieval of sets of feature vectors, given a known error process operating on the query set, that perturbs the query set away from the corresponding stored set. The algorithms operate by mapping each set to a corresponding generalized indicator vector and then performing a pruned search of a tree containing stored indicator vectors. The pruning is based on the probability of the query, given the stored items below the current position in the tree. Analysis and trial results show that this approach requires less total computation than existing methods based on parallel architectures. The indicator vector retrieval method can also cope efficiently with query vectors of much higher dimensionality than existing serial algorithms for nearest-neighbor searches