Title :
Convergence properties of a class of learning vector quantization algorithms
Author :
Kosmatopoulos, Elias B. ; Christodoulou, Manolis A.
Author_Institution :
Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece
fDate :
2/1/1996 12:00:00 AM
Abstract :
A mathematical analysis of a class of learning vector quantization (LVQ) algorithms is presented. Using an appropriate time-coordinate transformation, we show that the LVQ algorithms under consideration can be transformed into linear time-varying stochastic difference equations. Using this fact, we apply stochastic Lyapunov stability arguments, and we prove that the LVQ algorithms under consideration do indeed converge, provided that some appropriate conditions hold
Keywords :
Lyapunov methods; adaptive signal processing; convergence of numerical methods; difference equations; learning (artificial intelligence); stochastic processes; vector quantisation; LVQ algorithms; adaptive signal processing; convergence properties; learning vector quantization algorithms; linear time-varying stochastic difference equations; mathematical analysis; stochastic Lyapunov stability; time coordinate transformation; Convergence; Data compression; Drives; Neural networks; Partitioning algorithms; Probability distribution; Senior members; Signal processing algorithms; Stochastic processes; Vector quantization;
Journal_Title :
Image Processing, IEEE Transactions on
