t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections

Angelos Chatzimparmpas, Rafael M. Martins, Andreas Kerren

External link (DOI)
View presentation:2020-10-30T17:00:00ZGMT-0600Change your timezone on the schedule page
2020-10-30T17:00:00Z
Exemplar figure
Visual inspection with t-viSNE: (a) a panel for choosing between two execution modes; (b) overview of the results; (c) Shepard Heatmap of all pairwise distances; (d) histogram with the Density and Remaining Cost distributions; (e) list of available projections, ranked by quality; (f) main scatterplot view representing the Density of neighborhoods in the high-dimensional space and the Remaining Cost of each point; (g) Neighborhood Preservation bar chart; (h) different interaction modes; (i) visual mapping panel with a variety of options; (j) Dimension Correlation bar chart visualizing the correlations between dimensions; and (k) Adaptive PCP representing the most important dimensions.
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Direct link to video on YouTube: https://youtu.be/qDsItJiruvc

Keywords

Interpretable t-SNE, dimensionality reduction, high-dimensional data, explainable machine learning, visualization

Abstract

t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. Despite their usefulness, t-SNE projections canbe hard to interpret or even misleading, which hurts the trustworthiness of the results. Understanding the details of t-SNE itself and the reasons behind specific patterns in its output may be a daunting task, especially for non-experts in dimensionality reduction. In this work, we present t-viSNE, an interactive tool for the visual exploration of t-SNE projections that enables analysts to inspect different aspects of their accuracy and meaning, such as the effects of hyper-parameters, distance and neighborhood preservation, densities and costs of specific neighborhoods, and the correlations between dimensions and visual patterns. We propose a coherent, accessible, and well-integrated collection of different views for the visualization of t-SNE projections. The applicability and usability of t-viSNE are demonstrated through hypothetical usage scenarios with real data sets. Finally, we present the results of a user study where the tool's effectiveness was evaluated. By bringing to light information that would normally be lost after running t-SNE, we hope to support analysts in using t-SNE and making its results better understandable.