Print Email Facebook Twitter Approximated and User Steerable tSNE for Progressive Visual Analytics Title Approximated and User Steerable tSNE for Progressive Visual Analytics Author Pezzotti, N. (TU Delft Computer Graphics and Visualisation) Lelieveldt, B.P.F. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) van der Maaten, L.J.P. (TU Delft Pattern Recognition and Bioinformatics) Höllt, T. (TU Delft Computer Graphics and Visualisation) Eisemann, E. (TU Delft Computer Graphics and Visualisation) Vilanova Bartroli, A. (TU Delft Computer Graphics and Visualisation) Date 2016 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. Subject Approximate ComputationHigh Dimensional DataDimensionality ReductionProgressive Visual Analytics To reference this document use: http://resolver.tudelft.nl/uuid:6d108edf-05cf-497e-aec0-60b6ff1bbbd8 DOI https://doi.org/10.1109/TVCG.2016.2570755 ISSN 1077-2626 Source IEEE Transactions on Visualization and Computer Graphics, 23 (7), 1739-1752 Bibliographical note Accepted author manuscript Part of collection Institutional Repository Document type journal article Rights © 2016 N. Pezzotti, B.P.F. Lelieveldt, L.J.P. van der Maaten, T. Höllt, E. Eisemann, A. Vilanova Bartroli Files PDF tvcg16_approximated_and_u ... lytics.pdf 2.47 MB Close viewer /islandora/object/uuid:6d108edf-05cf-497e-aec0-60b6ff1bbbd8/datastream/OBJ/view