Title :
Convergence properties of SOFM algorithm for vector quantization
Author :
Lin, Siming ; Si, Jennie
Author_Institution :
Dept. of Electr. Eng., Arizona State Univ., Tempe, AZ, USA
Abstract :
In this paper, we present new results to the literature on convergence properties of the self-organizing feature map (SOFM) as a multi-dimensional vector quantizer using Robbins-Monro stochastic approximation principle. It is shown that the weights of the SOFM algorithm converge almost truly to the centroids of the cells of a Voronoi partition of the input space if the neighborhood function satisfies some reasonable conditions. The range of neighborhood functions in the SOFM algorithm is interpreted as a control parameter for an annealing process. Computer simulations were performed to demonstrate the convergence properties of the SOFM
Keywords :
convergence of numerical methods; self-organising feature maps; simulated annealing; vector quantisation; Robbins-Monro stochastic approximation; SOFM algorithm; Voronoi partition; annealing; computer simulation; control parameter; convergence; multi-dimensional vector quantization; neighborhood function; self-organizing feature map; Annealing; Application software; Computer simulation; Convergence; Markov processes; Mean square error methods; Speech processing; Stochastic processes; Topology; Vector quantization;
Conference_Titel :
Circuits and Systems, 1997. ISCAS '97., Proceedings of 1997 IEEE International Symposium on
Print_ISBN :
0-7803-3583-X
DOI :
10.1109/ISCAS.1997.608787
