Dimensionality reduction is a popular preprocessing and a widely used tool in data mining. Transparency, which is usually achieved by means of explanations, is nowadays a widely accepted and crucial requirement of machine learning based systems like classifiers and recommender systems. However, transparency of dimensionality reduction and other data mining tools have not been considered in much depth yet, still it is crucial to understand their behavior -- in particular practitioners might want to understand why a specific sample got mapped to a specific location. In order to (locally) understand the behavior of a given dimensionality reduction method, we introduce the abstract concept of contrasting explanations for dimensionality reduction, and apply a realization of this concept to the specific application of explaining two dimensional data visualization.

翻译：降维是一种流行的预处理步骤，也是数据挖掘中广泛使用的工具。通过解释实现的透明度，如今已被广泛接受为基于机器学习的系统（如分类器和推荐系统）的关键要求。然而，降维及其他数据挖掘工具的透明度尚未得到深入考虑，而理解其行为仍至关重要——特别是实践者可能想理解为何特定样本会被映射到特定位置。为了（局部地）理解给定降维方法的行为，我们引入了降维对比性解释的抽象概念，并将该概念的一种实现应用于解释二维数据可视化的具体场景。