Relation between fractal dimension and performance of vector quantization

K. Kumaraswamy, C. Faloutsos, Guoqiang Shan, V. Megalooikonomou
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Abstract

This paper shows that the performance of a vector quantizer is related to the intrinsic ("fractal") dimension of the data set for a perfectly self-similar object. Experiments are performed to confirm the result on synthetic and real data sets. Further to verify the result, we computed the slope and compared it to the estimate of the fractal dimension obtained using the correlation integral. However, the computation of the correlation fractal dimension is linear on the number of data points and significantly faster than vector quantization.
分形维数与矢量量化性能的关系
本文证明了矢量量化器的性能与完全自相似对象的数据集的内在维数(“分形”)有关。在合成数据集和实际数据集上进行了实验验证。为了进一步验证结果,我们计算了斜率,并将其与使用相关积分获得的分形维数估计值进行了比较。然而,相关分形维数的计算与数据点的数量呈线性关系,并且明显快于矢量量化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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