Projections, also known as dimensionality reduction (DR), are key techniques involved in creating visualizations of high dimensional data and, as such, present in the vast majority of visual analytics (VA) systems for explainable AI.
Yet, it is well known that projections cannot capture all aspects of large, complex, and high-dimensional data. To help users understanding what a projection can show (or not), many quality metrics have been designed. In this talk, I will present an overview of such quality metrics and ways to use them in actual VA exploratory workflows. More importantly, I will highlight some surprising, and not known, fundamental, limitations that most such quality metrics share.
These limitations expose deeper questions that relates to the actionable usage of projections as tools to understand high-dimensional data: When is one projection better than another one? When, and what for, can we actually use projections to reason about such data? What do we miss, in visualization research, to answer such questions?
Dia: 28/11/2025 – 14h às 15h
Local: Auditório – NUMEC