基于核密度估计的多元PDF匹配

D. Fantinato, L. Boccato, R. Attux, A. Neves
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引用次数: 10

摘要

本文提出了一种基于多变量概率密度函数(pdf)匹配的相似性度量方法。根据信息理论学习(ITL)框架,使用二次距离进行联合pdf之间的亲和比较,并借助于高斯核的Parzen窗口方法进行估计。这项建议的动机是提出一种标准,能够在很大程度上量化对具有时间和(或)空间结构的信息源的统计依赖性,例如音频、图像和编码数据。在一组盲均衡场景下,分析并比较了该措施与基于itl的标准方法-相关熵。比较包括表面分析、误码率方面的性能比较和图像处理方面的定性讨论。同样重要的是,该研究包括两种计算智能范式的应用:极端学习机和差分进化。结果表明,在某些情况下,该建议比相关系数更能提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multivariate PDF matching via kernel density estimation
In this work, a measure of similarity based on the matching of multivariate probability density functions (PDFs) is proposed. In consonance with the information theoretic learning (ITL) framework, the affinity comparison between the joint PDFs is performed using a quadratic distance, estimated with the aid of the Parzen window method with Gaussian kernels. The motivation underlying this proposal is to introduce a criterion capable of quantifying, to a significant extent, the statistical dependence present on information sources endowed with temporal and/or spatial structure, like audio, images and coded data. The measure is analyzed and compared with the canonical ITL-based approach - correntropy - for a set of blind equalization scenarios. The comparison includes elements like surface analysis, performance comparison in terms of bit error rate and a qualitative discussion concerning image processing. It is also important to remark that the study includes the application of two computational intelligence paradigms: extreme learning machines and differential evolution. The results indicate that the proposal can be, in some scenarios, a more informative formulation than correntropy.
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