A comparison between neural network and conventional vector quantization codebook algorithms

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.<>