Class-constrained t-SNE: Combining Data Features and Class Probabilities
Linhao Meng Stef van den Elzen Nicola Pezzotti Anna Vilanova
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.