A comparison between neural network and conventional vector quantization codebook algorithms

Abstract

Kohonen's (1988) unsupervised learning algorithm is successfully applied to the codebook generation problem. The algorithm has shown to provide a codebook that rivals the performance of the codebooks obtained using the conventional Linde-Buzo-Gray algorithm, while requiring a minimum amount of processing. The unsupervised learning algorithm provides the ability to adapt to changing inputs, something that is not possible with the standard algorithm. These features make Kohonen's unsupervised learning algorithm an attractive alternative to the conventional vector quantization codebook generation technique.<>