Convergence of Kohonen’s Learning Vector Quantization

Convergence of Kohonen’s Learning Vector Quantization

Title : Convergence of Kohonen's Learning Vector Quantization
Authors :
Baras, John S.
LaVigna, Anthony

Conference : The 1990 International Joint Conference on Neural Networks (IJCNN) pp. 17-20
Date: June 17 - June 21, 1991


Kohonen’s Learning Vector Quantization is a nonparametric classification scheme which classifies observations by comparing then to k; templates called Voronoi vectors. The locations of these vectors are determined from past labeled data through a learning algorithm. When learning is complete, the class of a new observation is the same as the class of the closest Voronoi vector. Hence LVQ is similar to nearest neighbors, except that instead of all of the past observations being searched only the k Voronoi vectors are searched.

In this paper, we show that the LVQ learning algorithm converges to locally asymptotic stable equilibria of an ordinary differential equation. It is shown that the learning algorithm performs stochastic approximation. Convergence of the vectors is guaranteed under the appropriate conditions on the underlying statistics of the classification problem. We also present a modification to the learning algorithm which results in more robust convergence.

Download Full Paper