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混合随机场中的可伸缩伪似然估计
Scalable Pseudo-Likelihood Estimation in Hybrid Random Fields |
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| 课程网址: |
http://videolectures.net/kdd09_freno_sple/
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| 主讲教师: |
Antonino Freno
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| 开课单位: |
锡耶纳大学
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| 开课时间: |
2009-09-14
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| 课程语种: |
英语
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| 中文简介: |
从高维数据集学习概率图形模型是计算上具有挑战性的任务。在许多有趣的应用中，域维度是为了防止现有技术的统计学习技术在合理的时间内提供准确的模型。本文提出了一种用于高维域伪似然估计的混合随机场模型。理论分析证明，混合随机场可表示的伪似然分布类严格地包括贝叶斯网络可表示的联合概率分布类。为了从数据中学习混合随机场，我们开发了Markov Blanket Merging算法。理论和实验证据表明，Markov Blanket Merging可以很好地扩展到高维数据集。与其他广泛使用的统计学习技术相比，Markov Blanket Merging在许多链路预测任务中提供了准确的结果，同时在计算效率方面也实现了显着的改进。我们在本文中调查的模型的软件实现在http公开。同一网站还托管了本工作中使用的数据集，这些数据集在我们的实验所使用的相同预处理中的其他地方不可用。
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| 课程简介: |
Learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such as to prevent state-of-the-art statistical learning techniques from delivering accurate models in reasonable time. This paper presents a hybrid random field model for pseudo-likelihood estimation in high-dimensional domains. A theoretical analysis proves that the class of pseudo-likelihood distributions representable by hybrid random fields strictly includes the class of joint probability distributions representable by Bayesian networks. In order to learn hybrid random fields from data, we develop the Markov Blanket Merging algorithm. Theoretical and experimental evidence shows that Markov Blanket Merging scales up very well to high-dimensional datasets. As compared to other widely used statistical learning techniques, Markov Blanket Merging delivers accurate results in a number of link prediction tasks, while achieving also significant improvements in terms of computational efficiency. Our software implementation of the models investigated in this paper is publicly available at . The same website also hosts the datasets used in this work that are not available elsewhere in the same preprocessing used for our experiments.
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| 关 键 词: |
高维数据集; 概率图形; 域维度
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| 课程来源: |
视频讲座网
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| 最后编审: |
2021-12-22：liyy
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| 阅读次数: |
104
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