Competitive learning algorithms for robust vector quantization

The efficient representation and encoding of signals with limited resources, e.g., finite storage capacity and restricted transmission bandwidth, is a fundamental problem in technical as well as biological information processing systems. Typically, under realistic circumstances, the encoding and communication of messages has to deal with different sources of noise and disturbances. We propose a unifying approach to data compression by robust vector quantization, which explicitly deals with channel noise, bandwidth limitations, and random elimination of prototypes. The resulting algorithm is able to limit the detrimental effect of noise in a very general communication scenario. In addition, the presented model allows us to derive a novel competitive neural networks algorithm, which covers topology preserving feature maps, the so-called neural-gas algorithm, and the maximum entropy soft-max rule as special cases. Furthermore, continuation methods based on these noise models improve the codebook design by reducing the sensitivity to local minima. We show an exemplary application of the novel robust vector quantization algorithm to image compression for a teleconferencing system.