Although the "neural-gas" network proposed by Martinetz et al. in 1993 has been proven for its optimality in vector quantizer design and has been demonstrated to have good performance in time-series prediction its high computational complexity (TVlogN) makes it a slow sequential algorithm. In this letter we suggest two ideas to speedup its sequential realization: 1) using a truncated exponential function as its neighborhood function and 2) applying a new extension of the partial distance elimination method (PDE). This fast realization is compared with the original version of the neural-gas network for codebook design in image vector quantization. The comparison indicates that a speedup of five times is possible while the quality of the resulting codebook is almost the same as that of the straightforward realization.
- Neural-gas network
- Partial distance elimination
- Vector quantization
ASJC Scopus subject areas
- Electrical and Electronic Engineering