A common workflow in data exploration is to learn a low-dimensional representation of the data, identify groups of points in that representation, and examine the differences between the groups to determine what they represent. We treat this as an interpretable machine learning problem by leveraging the model that learned the low-dimensional representation to help identify the key differences between the groups. To solve this problem, we introduce a new type of explanation, a Global Counterfactual Explanation (GCE), and our algorithm, Transitive Global Translations (TGT), for computing GCEs. TGT identifies the differences between each pair of groups using compressed sensing but constrains those pairwise differences to be consistent among all of the groups. Empirically, we demonstrate that TGT is able to identify explanations that accurately explain the model while being relatively sparse, and that these explanations match real patterns in the data.

介绍了一种新的解释机器学习问题的算法，利用学习到的低维度表示来识别不同群组之间的关键差异，该算法名为全局反事实解释，使用压缩感知技术限制差异保持一致。实验证明这种算法可以较为精确地解释模型，并与数据中的实际模式相匹配。

低维度表示中点的群组解释