Neural Autoassociative Memories for Binary Vectors: A Survey

discuss not only autoassociative memory, but also the generalization properties of these networks.We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints.Conclusions.In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research.An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors.