Fast sequential implementation of "neural-gas" network for vector quantization

Sze Tsan Choy, Wan Chi Siu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)301-304
Number of pages4
JournalIEEE Transactions on Communications
Volume46
Issue number3
DOIs
Publication statusPublished - 1 Dec 1998

Keywords

  • Neural-gas network
  • Partial distance elimination
  • Vector quantization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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