论文标题
基于部分信息分解的多元信息的近似方案
An Approximation Scheme for Multivariate Information based on Partial Information Decomposition
论文作者
论文摘要
我们考虑一个近似信息的近似方案，假设仅抑制了以高阶联合分布出现的协同信息，则可以在大型系统中保留。我们的近似方案提供了一种评估随机变量之间信息的实用方法，并有望应用于机器学习中的特征选择。我们近似方案的截断顺序由协同命令给出。在信息分类中，我们使用原始信息的部分信息分解。如果系统抑制了高阶协同作用，则预计所得的多元信息将是合理的。另外，如果截断顺序不那么大，则可以以相对简单的方式来计算。我们还执行数值实验以检查我们近似方案的有效性。
We consider an approximation scheme for multivariate information assuming that synergistic information only appearing in higher order joint distributions is suppressed, which may hold in large classes of systems. Our approximation scheme gives a practical way to evaluate information among random variables and is expected to be applied to feature selection in machine learning. The truncation order of our approximation scheme is given by the order of synergy. In the classification of information, we use the partial information decomposition of the original one. The resulting multivariate information is expected to be reasonable if higher order synergy is suppressed in the system. In addition, it is calculable in relatively easy way if the truncation order is not so large. We also perform numerical experiments to check the validity of our approximation scheme.
