Interpretable interactive visual pattern discovery in lossless 3D visualization is a promising way to advance machine learning. It enables end users who are not data scientists to take control of the model development process as a self-service. It is conducted in 3D General Line Coordinates (GLC) visualization space, which preserves all n-D information in 3D. This paper presents a system which combines three types of GLC: Shifted Paired Coordinates (SPC), Shifted Tripled Coordinates (STC), and General Line Coordinates-Linear (GLC-L) for interactive visual pattern discovery. A transition from 2-D visualization to 3-D visualization allows for a more distinct visual pattern than in 2-D and it also allows for finding the best data viewing positions, which are not available in 2-D. It enables in-depth visual analysis of various class-specific data subsets comprehensible for end users in the original interpretable attributes. Controlling model overgeneralization by end users is an additional benefit of this approach.

翻译：在无损三维可视化中进行可解释的交互式视觉模式发现，是推动机器学习发展的一个有前景的途径。它使非数据科学家的终端用户能够以自助服务的方式掌控模型开发过程。该研究在三维通用线性坐标（GLC）可视化空间中进行，该空间将所有n维信息无损保留于三维中。本文提出一个结合三种GLC类型的系统：移位配对坐标（SPC）、移位三重坐标（STC）和线性通用线性坐标（GLC-L），用于交互式视觉模式发现。从二维可视化到三维可视化的过渡，能够呈现出比二维更清晰的视觉模式，同时还能在三维中找到二维无法实现的较佳数据观察视角。这使得终端用户能够在原始可解释属性中，对各类别特定数据子集进行深入的视觉分析。终端用户对模型过度泛化现象的控制，是该方法的另一项附加优势。