This paper proposes a neural network that stores and retrieves sparse patterns categorically, the patterns being random realizations of a sequence of biased (0,1) Bernoulli trials. The neural network, denoted as categorizing associative memory, consists of two modules: 1) an adaptive classifier (AC) module that categorizes input data; and 2) an associative memory (AM) module that stores input patterns in each category according to a Hebbian learning rule, after the AC module has stabilized its learning of that category. We show that during training of the AC module, the weights in the AC module belonging to a category converge to the probability of a "1" occurring in a pattern from that category. This fact is used to set the thresholds of the AM module optimally without requiring any a priori knowledge about the stored patterns.
['Ferdinand Peper', 'Mehdi N. Shirazi']
A categorizing associative memory using an adaptive classifier and sparse coding