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Competitive learning algorithms for vector quantization

✍ Scribed by Stanley C. Ahalt; Ashok K. Krishnamurthy; Prakoon Chen; Douglas E. Melton

Publisher: Elsevier Science
Year: 1990
Tongue: English
Weight: 889 KB
Volume: 3
Category: Article
ISSN: 0893-6080
DOI: 10.1016/0893-6080(90)90071-r

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✦ Synopsis

We compare a number of training algorithms for competitive learning networks applied to the problem of vector quantization for data compression. A new competitive-learning algorithm based on the "conscience" learning method is introduced. The performance of competitive learning neural networks and traditional nonneural algorithms for vector quantization is compared. The basic properties of the algorithms are discussed and we present a number of examples that illustrate their use. The new algorithm is shown to be efficient and yields near-optimal results. This algorithm is used to design a vector quantizer fi>r a speech database. We conclude with a discttssion of continuing work.

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