An Auto-Associative Unit Memory Network

This paper describes a new auto-associative network called a Unit Memory network. It is so-called because unit nodes in a network store binary input as integer values, representing binary words. The word size is critical and specific to the dataset and it also provides a first level of cohesion over the word values. A second cohesion network then links the unit network nodes, through layers of decreasing dimension, until the top layer contains only 1 node for any pattern. Thus, a pattern can be found using a search and compare technique through the two networks. The unit memory is compared to a Hopfield network and a Sparse Distributed Memory (SDM). It is shown that the memory requirements are not unreasonable and that it has a much larger capacity than a discrete Hopfield network, for example. It can also store dense or sparse data and can deal with maybe 30% noise. This is demonstrated with test results for 3 benchmark datasets. Apart from unit size, the rest of the configuration is automatic, and its simplistic design could make it attractive for some applications.