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Binary Factorization in Hopfield-Like Neural Autoassociator: A Promising Tool for Data Compression
- 1.0404887 - UIVT-O 20030184 RIV AT eng C - Konferenční příspěvek (zahraniční konf.)
Frolov, A. A. - Sirota, A.M. - Húsek, Dušan - Muraviev, I. - Combe, P.
Binary Factorization in Hopfield-Like Neural Autoassociator: A Promising Tool for Data Compression.
Artificial Neural Nets and Genetic Algorithms. Wien: SpringerVerlag, 2003 - (Pearson, D.; Steele, N.; Albrecht, R.), s. 58-62. ISBN 3-211-00743-1.
[ICANNGA'2003 /6./. Roanne (FR), 23.04.2003-25.04.2003]
Grant CEP: GA ČR GA201/01/1192
Grant ostatní: FR-CZ Barrande(XX) 04693QG
Výzkumný záměr: AV0Z1030915
Klíčová slova: neural networks * binary factorization * data compression
Kód oboru RIV: JD - Využití počítačů, robotika a její aplikace
Proposed approach of data compression is based on feature extraction procedure which maps original patterns into features (factors) space of reduced, possibily very small, dimension. It is shown that Hebbian unsupervised learning of Hopfield-like neural network is a natural procedure for factor extraction. Due to this learning, factors become the attractors of network dynamics, hence they can be revealed by the random search.
Trvalý link: http://hdl.handle.net/11104/0125111
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