VTK高斯混合模型的期望最大化

The VTK Journal Pub Date : 2010-09-21 DOI:10.54294/7bon9f

D. Doria

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引用次数: 0

摘要

期望最大化(EM)是在收集模型生成的数据的观测值后估计模型参数的常用技术。我们首先解释算法，然后给出我们的实现。我们主要研究高斯混合模型(GMM)的参数估计。实现是在VTK框架中编写的，并作为一个新类vtkExpectationMaximization提供。代码暂时托管在这里:http://github.com/daviddoria/ExpectationMaximization。

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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Expectation Maximization of Gausian Mixture Models in VTK

Expectation maximization (EM) is a common technique for estimating the parameters of a model after having collected observations of data generated by the model. We first explain the algorithm, then present our impelementation. We focus on estimation of the parameters of a Gaussian Mixture Model (GMM). The implementation is written in the VTK framework and is provided as a new class, vtkExpectationMaximization.The code is hosted here: http://github.com/daviddoria/ExpectationMaximization for the time being.

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来源期刊

The VTK Journal

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