论文标题
密度破坏者中的信息理论
Information Theory in Density Destructors
论文作者
论文摘要
密度破坏者是可差的且可逆的变换，可将任意结构的多元PDF（低熵）映射到非结构化PDF（最大熵）中。多元高斯化和多元均衡是该家族的特定示例，它通过一组基本变换分解了原始PDF的复杂性，这些转换逐渐消除了数据的结构。我们展示了这种密度破坏性流的特性如何连接到经典信息理论，以及如何使用密度破坏者来获得更准确的信息理论量估计。与竞争方法相比，具有总相关性和共同信息不变的实验表明了密度破坏者的能力。这些结果表明，当学习密度破坏性流动时，信息理论措施可能是替代优化标准。
Density destructors are differentiable and invertible transforms that map multivariate PDFs of arbitrary structure (low entropy) into non-structured PDFs (maximum entropy). Multivariate Gaussianization and multivariate equalization are specific examples of this family, which break down the complexity of the original PDF through a set of elementary transforms that progressively remove the structure of the data. We demonstrate how this property of density destructive flows is connected to classical information theory, and how density destructors can be used to get more accurate estimates of information theoretic quantities. Experiments with total correlation and mutual information inmultivariate sets illustrate the ability of density destructors compared to competing methods. These results suggest that information theoretic measures may be an alternative optimization criteria when learning density destructive flows.
