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 much 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：降低维度是一种流行的预处理,也是数据开采中广泛使用的工具。透明度通常是通过解释实现的。透明度现在已成为基于机器学习的系统,如分类和建议系统的广泛接受和关键要求。然而,对于减少维度的透明度和其他数据挖掘工具,还没有多少考虑,但了解其行为仍然至关重要,特别是从业人员可能想了解为什么具体样本被绘制到特定位置。为了(当地)了解特定维度减少方法的行为,我们引入了对减少维度进行对比解释的抽象概念,并将这一概念的实现应用于解释两个维度数据可视化的具体应用。