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A unified approach to selecting optimal step lengths for adaptive vector quantizers

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2 Author(s)
Andrew, L.L.H. ; Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia ; Palaniswami, M.

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 provide a measurable improvement over results which were previously thought to be optimal

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Communications, IEEE Transactions on  (Volume:44 ,  Issue: 4 )