Abstract
A recently introduced technique of topology sensitivity in method of moments is extended by the possibility of adding degrees of freedom (reconstruct) into the underlying structure. The algebraic formulation is inversion-free, suitable for parallelization, and scales favorably with the number of unknowns. The reconstruction completes the nearest neighbors procedure for an evaluation of the smallest shape perturbation. The performance of the method is studied with a greedy search over a Hamming graph representing the structure in which initial positions are chosen from a random set. The method is shown to be an effective data mining tool for machine learning-related applications.
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