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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/44076
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- A unified approach to selecting optimal step lengths for adaptive vector quantizers
- Andrew, Lachlan L. H.; Palaniswami, Marimuthu
- This paper presents expressions for the optimal step length to use when training a vector quantizer by stochastic approximation. By treating each update as an estimation problem it provides a unified framework covering both batch and incremental training which were previously treated separately and extends existing results to the semibatch case. In addition the new results presented here provide a measurable improvement over results which were previously thought to be optimal.
- Publication type
- Journal article
- IEEE Transactions on Communications, Vol. 44, no. 4 (Apr 1996), pp. 434-439
- Publication year
- Adaptive systems; Adaptive vector quantizer; Approximation theory; Batch training; Data compression; Incremental training; Learning systems; Minimum variance unbiased estimator; Optimal step lengths; Optimization; Parameter estimation; Probability density function; Random processes; Vector quantization
- Publisher URL
- Copyright © 1996 IEEE. Paper reproduced here in accordance with the copyright policy of the publisher.
- Full text
- Peer reviewed