Kohonen maps versus vector quantization for data analysis

Besides their topological properties, Kohonen maps are often used for vector quantization only. These auto-organised networks are often compared to other standard and/or adaptive vector quantization methods, and, according to the large literature on the subject, show either better or worst properties in terms of quantization, speed of convergence, approximation of probability densities, clustering,… The purpose of this paper is to define more precisely some commonly encountered problems, and to try to give some answers through well-known theoretical arguments or simulations on simple examples.

• Type: Conference
• Year: 1997
• Where: ESANN
• Authors: Eric de Bodt, Michel Verleysen, Marie Cottrell
• Keywords: Adaptive Vector Quantization, ESANN 1997, ESANN 2007, Vector Quantization, Well-known Theoretical Arguments