Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling
Peter Tino, Nikolaos Gianniotis
Recently, generative probabilistic modeling principles were extended to visualization of structured data types, such as sequences. The models are formulated as constrained mixtures of sequence models - a generalization of density-based visualization methods previously developed for static data sets. In order to effectively explore visualization plots, one needs to understand local directional magnification factors, i.e. the extend to which small positional changes on visualization plot lead to changes in local noise models explaining the structured data. Magnification factors are useful for highlighting boundaries between data clusters. In this paper we present two techniques for estimating local metric induced on the sequence space by the model formulation. We first verify our approach in two controlled experiments involving artificially generated sequences. We then illustrate our methodology on sequences representing chorals by J.S. Bach.