Unsupervised DeepView: Global Uncertainty Visualization for High Dimensional Data

Carina Newen, Emmanuel Müller
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Abstract

In recent years, more and more visualization methods for explanations of artificial intelligence have been proposed that focus on untangling black box models for single instances of the data set. While the focus often lies on supervised learning algorithms, the study of uncertainty estimations in the unsupervised domain for high-dimensional data sets in the explainability domain has been neglected so far. As a result, existing visualization methods struggle to visualize global uncertainty patterns over whole datasets. We propose Unsupervised DeepView, the first global uncertainty visualization method for high dimensional data based on a novel unsupervised proxy for local uncertainties. In this paper, we exploit the mathematical notion of local intrinsic dimensionality as a measure of local data complexity. As a label-agnostic measure of model uncertainty in unsupervised machine learning, it shows two highly desirable features: It can be used for global structure visualization as well as for the detection of local adversarials. In our empirical evaluation, we demonstrate its ability both in visualizations and quantitative analysis for unsupervised models on multiple datasets.
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无监督深度视图:高维数据的全局不确定性可视化
近年来,越来越多用于解释人工智能的可视化方法被提出,这些方法的重点是解开数据集单个实例的黑箱模型。在监督学习算法的研究中,对高维数据集在可解释性域中的无监督域的不确定性估计的研究一直被忽视。因此,现有的可视化方法难以在整个数据集上可视化全局不确定性模式。我们提出了Unsupervised DeepView,这是第一个基于局部不确定性的新型无监督代理的高维数据全局不确定性可视化方法。在本文中,我们利用局部固有维数的数学概念作为局部数据复杂性的度量。作为无监督机器学习中模型不确定性的标签不可知度量,它显示了两个非常理想的特征:它可以用于全局结构可视化以及局部对手的检测。在我们的经验评估中,我们证明了它在多个数据集上的无监督模型的可视化和定量分析方面的能力。
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