具有最优边际parzen密度估计和高斯化的多元密度估计

信号处理 Pub Date : 2004-09-29 DOI:10.1109/MLSP.2004.1422961

Deniz Erdoğmuş, R. Jenssen, Y. Rao, J. Príncipe

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引用次数: 12

摘要

多元密度估计是统计学习和信号处理中经常遇到的一个重要问题。最流行的技术之一是Parzen窗口，也称为核密度估计。高斯化是一种允许人们从单个随机变量的边际密度有效地估计多元密度的过程。在本文中，我们提出了一种最优密度估计方案，该方案结合了Parzen窗口和高斯化的理想特性，使用最小Kullback-Leibler散度作为选择Parzen窗口步骤核大小的最优准则。通过一个分类器设计实例说明了该估计的性能

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Multivariate density estimation with optimal marginal parzen density estimation and gaussianization

Multivariate density estimation is an important problem that is frequently encountered in statistical learning and signal processing. One of the most popular techniques is Parzen windowing, also referred to as kernel density estimation. Gaussianization is a procedure that allows one to estimate multivariate densities efficiently from the marginal densities of the individual random variables. In this paper, we present an optimal density estimation scheme that combines the desirable properties of Parzen windowing and Gaussianization, using minimum Kullback-Leibler divergence as the optimality criterion for selecting the kernel size in the Parzen windowing step. The performance of the estimate is illustrated in a classifier design example

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来源期刊

信号处理

自引率

0.00%

发文量

5812

期刊介绍： Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.