Paper Info
Title
Non-Greedy Adaptive Vector Quantizers
Abstract
Kohonen's Learning Vector Quantization (LVQ) technique easily gets trapped in local minima of the distortion surface, resulting suboptimal vector quantizers. The reason is that the behavior of competitive learning on which the LVQ bases is greedy, that is, it only accepts new solutions which maximally reduce the distortion. In this paper, a new and non-greedy adaptive vector quantization scheme is developed which applied a simulated annealing — a randomized search algorithm to the learning procedure and has the capabilities of hill-climbing and approaching global optima. Therefore, this scheme has the advantage of global optimization over the Kohonen's LVQ scheme. The adaptation (learning) equations are derived and the design schedule procedure is presented.
Year
DOI
Venue
1993
10.1007/3-540-56798-4_171
IWANN
Keywords
DocType
ISBN
non-greedy adaptive vector quantizers,simulated annealing,learning vector quantization,global optimization,hill climbing,random search,local minima,adaptive learning,competitive learning
Conference
3-540-56798-4
Citations 
PageRank 
References 
0
0.34
4
Authors
1
Name
Order
Citations
PageRank
Zhicheng Wang117617.00