论文标题
基于自组织地图的两样本测试
Two-sample test based on Self-Organizing Maps
论文作者
论文摘要
机器学习分类器可以作为两样本统计测试的利用。假设每个样本都分配了一个不同的标签，并且分类器可以获得比判断它们更好的结果。在这种情况下，我们可以推断两个样本源自不同人群。但是，许多类型的模型，例如神经网络，都是用户的黑框：他们可以拒绝这两个样本源于同一人群，但它们并没有洞悉两个样本如何不同。自组织地图是最初设计为一个数据可视化工具的维度降低，该工具显示出紧急属性，也可用于分类任务。由于它们可以用作分类器，因此也可以用作两样本统计测试。但是由于他们最初的目的是可视化，因此他们也可以提供见解。
Machine-learning classifiers can be leveraged as a two-sample statistical test. Suppose each sample is assigned a different label and that a classifier can obtain a better-than-chance result discriminating them. In this case, we can infer that both samples originate from different populations. However, many types of models, such as neural networks, behave as a black-box for the user: they can reject that both samples originate from the same population, but they do not offer insight into how both samples differ. Self-Organizing Maps are a dimensionality reduction initially devised as a data visualization tool that displays emergent properties, being also useful for classification tasks. Since they can be used as classifiers, they can be used also as a two-sample statistical test. But since their original purpose is visualization, they can also offer insights.
