New generalized learning vector quantization alogorithm

Shui-Sheng Zhou; Li-Hua Zhou; Wei-Guang Liu

doi:10.1117/12.477116

31 July 2002 New generalized learning vector quantization alogorithm

Shui-Sheng Zhou, Li-Hua Zhou, Wei-Guang Liu

Proceedings Volume 4875, Second International Conference on Image and Graphics; (2002) https://doi.org/10.1117/12.477116
Event: Second International Conference on Image and Graphics, 2002, Hefei, China

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.

Citation Download Citation

Shui-Sheng Zhou, Li-Hua Zhou, and Wei-Guang Liu "New generalized learning vector quantization alogorithm", Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002); https://doi.org/10.1117/12.477116

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