Fast sequential implementation of "neural-gas" network for vector quantization
- 1 March 1998
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Communications
- Vol. 46 (3) , 301-304
- https://doi.org/10.1109/26.662634
Abstract
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.Department of Electronic and Information EngineerinKeywords
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