On the Use of Self-Organizing Maps to Accelerate Vector Quantization

Self-organizing maps (SOM) are widely used for their topology preservation property: neighboring input vectors are quantified (or classified) either on the same location either on neighboring ones on a predefined grid. But SOM are also widely used for their more classical vector quantization property. We show in this paper that using SOM instead of the more classical Simple Competitive Learning (SCL) algorithm drastically increases the speed of convergence of the vector quantization process. We also suggest to use the result of SOM as initial conditions for the SCL algorithm, in order to benefit both from the increased convergence speed and the convergence towards optimal states