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Proceedings Paper

New generalized learning vector quantization alogorithm
Author(s): Shui-Sheng Zhou; Li-Hua Zhou; Wei-Guang Liu
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Paper Abstract

The disadvantage of the generalized learning vector quantization (GLVQ) and fuzzy generalization learning vector quantization (FGLVQ) algorithms is discussed. A revised GLVQ (RGLVQ) algorithm is proposed. Because the iterative coefficients of the proposed algorithms are properly bounded, the performance of our algorithms is invariant under uniform scaling of the entire data set unlike Pal's GLVQ, and the initial learning rate is not sensitive to the number of prototypes as Karayiannis's FGLVQ. The proposed algorithms are tested and evaluated using the iRIS data set. The efficiency of the proposed algorithms is also illustrated by their use in codebook design required for image compression based on vector quantization. The training time of RGLVQ algorithm is reduced by 20% as compared with Karayiannis's FGLVQ but the performance is similar.

Paper Details

Date Published: 31 July 2002
PDF: 7 pages
Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002); doi: 10.1117/12.477116
Show Author Affiliations
Shui-Sheng Zhou, Xidian Univ. (China)
Li-Hua Zhou, Xidian Univ. (China)
Wei-Guang Liu, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 4875:
Second International Conference on Image and Graphics
Wei Sui, Editor(s)

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