Manz Trevor, Lekschas Fritz, Greene Evan, Finak Greg, Gehlenborg Nils
IEEE Trans Vis Comput Graph. 2025 Jan;31(1):283-293. doi: 10.1109/TVCG.2024.3456370. Epub 2024 Dec 3.
Projecting high-dimensional vectors into two dimensions for visualization, known as embedding visualization, facilitates perceptual reasoning and interpretation. Comparing multiple embedding visualizations drives decision-making in many domains, but traditional comparison methods are limited by a reliance on direct point correspondences. This requirement precludes comparisons without point correspondences, such as two different datasets of annotated images, and fails to capture meaningful higher-level relationships among point groups. To address these shortcomings, we propose a general framework for comparing embedding visualizations based on shared class labels rather than individual points. Our approach partitions points into regions corresponding to three key class concepts-confusion, neighborhood, and relative size-to characterize intra- and inter-class relationships. Informed by a preliminary user study, we implemented our framework using perceptual neighborhood graphs to define these regions and introduced metrics to quantify each concept. We demonstrate the generality of our framework with usage scenarios from machine learning and single-cell biology, highlighting our metrics' ability to draw insightful comparisons across label hierarchies. To assess the effectiveness of our approach, we conducted an evaluation study with five machine learning researchers and six single-cell biologists using an interactive and scalable prototype built with Python, JavaScript, and Rust. Our metrics enable more structured comparisons through visual guidance and increased participants' confidence in their findings.
将高维向量投影到二维空间进行可视化，即嵌入可视化，有助于感知推理和解释。比较多个嵌入可视化可推动许多领域的决策，但传统的比较方法受限于对直接点对应关系的依赖。这种要求排除了没有点对应关系的比较，比如两个不同的带注释图像数据集，并且无法捕捉点组之间有意义的更高层次关系。为解决这些缺点，我们提出了一个基于共享类标签而非单个点来比较嵌入可视化的通用框架。我们的方法将点划分为对应三个关键类概念（混淆、邻域和相对大小）的区域，以表征类内和类间关系。基于初步的用户研究，我们使用感知邻域图来定义这些区域并引入度量来量化每个概念，从而实现了我们的框架。我们通过机器学习和单细胞生物学的使用场景展示了我们框架的通用性，突出了我们的度量在跨标签层次结构进行有洞察力比较方面的能力。为评估我们方法的有效性，我们使用由Python、JavaScript和Rust构建的交互式可扩展原型，对五名机器学习研究人员和六名单细胞生物学家进行了一项评估研究。我们的度量通过视觉引导实现了更结构化的比较，并增强了参与者对其发现的信心。
