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    Visualizing Time-Dependent Data Using Dynamic t-SNE

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    Date
    2016
    Author
    Rauber, Paulo E.
    Falcão, Alexandre X.
    Telea, Alexandru C.
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    Abstract
    Many interesting processes can be represented as time-dependent datasets. We define a time-dependent dataset as a sequence of datasets captured at particular time steps. In such a sequence, each dataset is composed of observations (high-dimensional real vectors), and each observation has a corresponding observation across time steps. Dimensionality reduction provides a scalable alternative to create visualizations (projections) that enable insight into the structure of such datasets. However, applying dimensionality reduction independently for each dataset in a sequence may introduce unnecessary variability in the resulting sequence of projections, which makes tracking the evolution of the data significantly more challenging. We show that this issue affects t-SNE, a widely used dimensionality reduction technique. In this context, we propose dynamic t-SNE, an adaptation of t-SNE that introduces a controllable trade-off between temporal coherence and projection reliability. Our evaluation in two time-dependent datasets shows that dynamic t-SNE eliminates unnecessary temporal variability and encourages smooth changes between projections.
    BibTeX
    @inproceedings {10.2312:eurovisshort.20161164,
    booktitle = {EuroVis 2016 - Short Papers},
    editor = {Enrico Bertini and Niklas Elmqvist and Thomas Wischgoll},
    title = {{Visualizing Time-Dependent Data Using Dynamic t-SNE}},
    author = {Rauber, Paulo E. and Falcão, Alexandre X. and Telea, Alexandru C.},
    year = {2016},
    publisher = {The Eurographics Association},
    ISSN = {-},
    ISBN = {978-3-03868-014-7},
    DOI = {10.2312/eurovisshort.20161164}
    }
    URI
    http://dx.doi.org/10.2312/eurovisshort.20161164
    https://diglib.eg.org:443/handle/10
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA