Title :
A unified approach to selecting optimal step lengths for adaptive vector quantizers
Author :
Andrew, Lachlan L H ; Palaniswami, Marimuthu
Author_Institution :
Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
fDate :
4/1/1996 12:00:00 AM
Abstract :
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
Keywords :
adaptive signal processing; approximation theory; optimisation; stochastic processes; vector quantisation; adaptive vector quantizers; batch training; estimation problem; incremental training; optimal step lengths selection; semibatch training; stochastic approximation; update; vector quantizer training; Communications Society; Data compression; Distortion measurement; Mean square error methods; Nearest neighbor searches; Noise level; Probability density function; Quantization; Stochastic processes; Training data;
Journal_Title :
Communications, IEEE Transactions on
