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首页> 外文期刊>電子情報通信学会技術研究報告. パターン認識·メディア理解. Pattern Recognition and Media Understanding >Experimental study for replacing statistics of posterior with propagated belief in the EM algorithm on lattice model
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Experimental study for replacing statistics of posterior with propagated belief in the EM algorithm on lattice model

机译：莱迪思模型中EM算法替代繁殖信仰统计的实验研究

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In this paper, we show results replacing the covariance of the posterior probability with the variance of the belief in the expectation maximization (EM) algorithm. We consider graphical models with hidden variables as Gaussian Markov random fields (GMRF) model, which is popular as a regularization term for solving ill-posed problem and/or for obtaining a smoothed solution of observed variables in the area of image recognition and pattern recognition. To solve this problem regarded as the maximum a posteriori (MAP) estimation, calculating the inverse covariance matrix with (number of pixels)×(number of pixels) is required, and it occurs enormous computational cost. Moreover, EM algorithm is commonly used for obtaining model parameters in the above area. For reducing the computational cost, we consider applying the belief obtained from BF scheme as an approximation of the posterior probability in the EM algorithm. We show some experimental results for estimator of model parameters in two different graphical models with GMRF.
机译：在本文中，我们展示了替代后概率的协方差与信仰在期望最大化（EM）算法中的方差。我们将带有隐藏变量的图形模型视为高斯马尔可夫随机字段（GMRF）模型，这是一种作为解决弊端问题和/或用于在图像识别领域获得所观察变量的平滑解决方案的正则化术语。 。为了解决作为最大后验（MAP）估计的该问题，需要计算具有（像素数）×（像素数）的逆协方差矩阵，并且它发生巨大的计算成本。此外，EM算法通常用于获得上述区域中的模型参数。为了降低计算成本，我们考虑将从BF方案获得的信仰应用于EM算法中的后验概率的近似值。我们对GMRF的两种不同图形模型中模型参数的估算结果显示了一些实验结果。

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