Data features and class probabilities are two main perspectives when, e.g., evaluating model results and identifying problematic items. Class probabilities represent the likelihood that each instance belongs to a particular class, which can be produced by probabilistic classifiers or even human labeling with uncertainty. Since both perspectives are multi-dimensional data, dimensionality reduction (DR) techniques are commonly used to extract informative characteristics from them. However, existing methods either focus solely on the data feature perspective or rely on class probability estimates to guide the DR process. In contrast to previous work where separate views are linked to conduct the analysis, we propose a novel approach, class-constrained t-SNE, that combines data features and class probabilities in the same DR result. Specifically, we combine them by balancing two corresponding components in a cost function to optimize the positions of data points and iconic representation of classes -- class landmarks. Furthermore, an interactive user-adjustable parameter balances these two components so that users can focus on the weighted perspectives of interest and also empowers a smooth visual transition between varying perspectives to preserve the mental map. We illustrate its application potential in model evaluation and visual-interactive labeling. A comparative analysis is performed to evaluate the DR results.

翻译：数据特征和类别概率是评估模型结果和识别问题样本时的两个主要视角。类别概率表示每个样本属于特定类别的可能性，可由概率分类器生成，甚至可通过带有不确定性的标注获得。由于这两个视角均属于多维数据，降维技术常被用于从中提取有效特征。然而，现有方法要么仅关注数据特征视角，要么依赖类别概率估计来指导降维过程。与以往通过关联不同视图进行分析的工作不同，我们提出了一种新颖方法——类别约束t-SNE，该方法在同一降维结果中融合了数据特征与类别概率。具体而言，我们通过在代价函数中平衡两个对应分量来优化数据点位置与类别标志性表示（即类别锚点），从而实现两者的融合。此外，一个可交互调节的用户参数可实现这两个分量的平衡，使用户能够聚焦于目标加权视角，同时支持在不同视角间平滑可视化过渡以保持认知地图。我们展示了该方法在模型评估和可视化交互标注中的应用潜力，并通过对比分析评估了降维结果。