论文标题
“为什么在这里而不在那里？” - 降低维度降低的各种对比解释
"Why Here and Not There?" -- Diverse Contrasting Explanations of Dimensionality Reduction
论文作者
论文摘要
降低降低是一种流行的预处理，也是数据挖掘中广泛使用的工具。如今，透明度通常是通过解释来实现的，它是基于机器学习的系统（例如分类器和推荐系统）的广泛接受和关键的要求。但是，降低维度和其他数据挖掘工具的透明度尚未深入考虑，但要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特定的样本被映射到特定位置。 为了（本地）了解给定维度降低方法的行为，我们介绍了降低维度的对比解释的抽象概念，并将实现此概念的实现应用于解释两个维数据可视化的特定应用。
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.
