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Efficient retrieval from sparse associative memory

✍ Scribed by Ronald L. Greene

Publisher: Elsevier Science
Year: 1994
Tongue: English
Weight: 816 KB
Volume: 66
Category: Article
ISSN: 0004-3702
DOI: 10.1016/0004-3702(94)90032-9

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✦ Synopsis

Best-match retrieval of data from memory which is sparse in feature space is a timeconsuming process for sequential machines. Previous work on this problem has shown that a connectionist network used as a hashing function can allow faster-than-linear probabilistic retrieval from such memory when presented with probing feature vectors which are noisy or partially specified. This paper introduces two simple modifications to the basic Connectionist-Hashed Associative Memory which together can improve the retrieval efficiency by an order of magnitude or more. Theoretical results are presented for storage/retrieval of memory items represented by feature vectors made up of 1000 randomly selected bivalent components. Experimental results on correlated feature vectors are presented in the context of a spelling correction application.

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