Approximated and User Steerable tSNE for Progressive Visual Analytics

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...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics Vol. 23; no. 7; pp. 1739 - 1752
Main Authors: Pezzotti, Nicola, Lelieveldt, Boudewijn P. F., Van Der Maaten, Laurens, Hollt, Thomas, Eisemann, Elmar, Vilanova, Anna
Format: Journal Article
Language:English
Published: United States IEEE 01.07.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Analytics
approximate computation
Approximation
Approximation algorithms
Computational complexity
Data analysis
Data visualization
Dimensional analysis
dimensionality reduction
High dimensional data
Mathematical analysis
progressive visual analytics
Real time
Real-time systems
Visual analytics
Visualization
ISSN:1077-2626, 1941-0506
Online Access:Get full text
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Summary: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.
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ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2016.2570755