Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19857
Title: Fast sequential implementation of "neural-gas" network for vector quantization
Authors: Choy, CST
Siu, WC 
Issue Date: 1998
Source: IEEE transactions on communications, 1998, v. 46, no. 3, p. 301-304
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.
Keywords: Neural-gas network
Partial distance elimination
Vector quantization
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE transactions on communications 
ISSN: 0090-6778
EISSN: 1558-0857
DOI: 10.1109/26.662634
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