Coefficient Focusing for the Moore-Penrose Associative Memory

This paper describes a modification of the Moore-Penrose distributed associative memory (DAM). The modification lies in the inclusion of an iterative scheme to increase the selectivity of the memory, and in the inclusion of a reject option. The reject option is indispensible when the DAM is used in a dynamic vision environment. The DAM can be applied for recognition of two-dimensional objects. When an unknown object is presented to the memory it outputs an associated image and a set of association coefficients. Previous applications used the magnitude of these coefficients to indicate the strength of association with each of the stored images. By exploiting the formal similarity of the DAM recollection scheme with multiple linear regression we suggest to use statistical significance measures instead to measure the strength of association. We further suggest an iterative scheme to improve the selectivity of the association coefficiants. This scheme is based on discarding from further consideration response vectors deemed to be insignificant. Experimental results prove the feasibility and benefits of the new recognition method.