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arXiv 1808.00566cs.ITmath.IT
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森林数据学习及其通用编码

Forest Learning from Data and its Universal Coding

Joe Suzuki

摘要:

本文考虑从具有$p$变量的$n$样本的数据中进行结构学习,假设结构是森林,使用Chow-Liu算法。具体来说,对于不完整的数据,我们构建了两个模型选择算法,完成$O(p^2)$步骤:一个获得森林的最大后验概率给定的数据,和其他获得的森林收敛到真正的$n$增加。我们表明,这两个森林一般是不同的,当一些值丢失。此外,我们提出了基准数据集的估计,以证明这两种算法在现实情况下的工作。此外,我们推导出的条件熵,提供没有值丢失,我们评估的不完整数据的通用编码的非丢失样本的数量方面的每样本的预期冗余。

英文摘要:

This paper considers structure learning from data with $n$ samples of $p$ variables, assuming that the structure is a forest, using the Chow-Liu algorithm. Specifically, for incomplete data, we construct two model selection algorithms that complete in $O(p^2)$ steps: one obtains a forest with the maximum posterior probability given the data, and the other obtains a forest that converges to the true one as $n$ increases. We show that the two forests are generally different when some values are missing. Additionally, we present estimations for benchmark data sets to demonstrate that both algorithms work in realistic situations. Moreover, we derive the conditional entropy provided that no value is missing, and we evaluate the per-sample expected redundancy for the universal coding of incomplete data in terms of the number of non-missing samples.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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