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A competitive and selective learning method for designing optimal vector quantizers

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2 Author(s)
Ueda, N. ; NTT Commun. Sci. Lab., Kyoto, Japan ; Nakano, R.

A new competitive learning method with a `selection' mechanism is proposed for the design of optimal vector quantizers. A basic principle called the `equidistortion principle' for designing optimal quantizers is derived theoretically, and a new learning algorithm based on this principle is presented. Unlike conventional algorithms based on the `conscience' mechanism, the proposed algorithm can minimize distortion without a particular initialization procedure, even when the input data cluster in a number of regions in the input vector space. The performance of this method is compared with that of the conscience learning method

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Neural Networks, 1993., IEEE International Conference on

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