A Robust Subspace Projection Autoassociative Memory Based on the M-Estimation Method

摘要

An autoassociative memory (AM) that projects an input pattern onto a linear subspace is referred to as a subspace projection AM (SPAM). The optimal linear AM (OLAM), which can be used for the storage and recall of real-valued patterns, is an example of SPAM. In this brief we introduce a novel SPAM model based on the robust M-estimation method. In contrast to the OLAM and many other associative memory models, the robust SPAM represents a neural network in which the synaptic weights are iteratively adjusted during the retrieval phase. Computational experiments concerning the reconstruction of corrupted gray-scale images reveal that the novel memories exhibit an excellent tolerance with respect to salt and pepper noise as well as some tolerance with respect to Gaussian noise and blurred input images.