针对小样本场景，提出更精准的香农熵估计方法。
Partitioning the Sample Space for a More Precise Shannon Entropy Estimation
- 利用可分解性与未见事件估计补偿负偏差
- 在数据不足时显著优于经典估计器
- 适合小样本熵估计场景，如生物信息学
从少量样本中可靠估计香农熵，在多个应用中至关重要，尤其当样本数小于可能结果数时。本文提出一种离散熵估计器，结合可分解性特性，通过估计缺失质量与未见结果数量来补偿由此产生的负偏差。实验表明，该方法在数据稀疏情形下优于部分经典估计器，且性能与一些先进主流估计器相当。
原文摘要 · Abstract (English)
Reliable data-driven estimation of Shannon entropy from small data sets, where the number of examples is potentially smaller than the number of possible outcomes, is a critical matter in several applications. In this paper, we introduce a discrete entropy estimator, where we use the decomposability property in combination with estimations of the missing mass and the number of unseen outcomes to compensate for the negative bias induced by them. Experimental results show that the proposed method outperforms some classical estimators in undersampled regimes, and performs comparably with some well-established state-of-the-art estimators.
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