Approximated and User Steerable tSNE for Progressive Visual Analytics
Nicola Pezzotti (TU Delft - Computer Graphics and Visualisation)
B. Lelieveldy (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)
L.J.P. van der Maaten (TU Delft - Pattern Recognition and Bioinformatics)
T. Höllt (TU Delft - Computer Graphics and Visualisation)
E. Eisemann (TU Delft - Computer Graphics and Visualisation)
A. Vilanova Bartroli (TU Delft - Computer Graphics and Visualisation)
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Abstract
Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One key method for data analysis is dimensionality reduction, for example, to produce 2D embeddings that can be visualized and analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a well-suited technique for the visualization of high-dimensional data. tSNE can create meaningful intermediate results but suffers from a slow initialization that constrains its application in Progressive Visual Analytics. We introduce a controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration. We offer real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation. With this feedback, the user can decide on local refinements and steer the approximation level during the analysis. We demonstrate our technique with several datasets, in a real-world research scenario and for the real-time analysis of high-dimensional streams to illustrate its effectiveness for interactive data analysis.