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    Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition

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    Date
    2021
    Author
    Pulido, Jesus
    Patchett, John
    Bhattarai, Manish
    Alexandrov, Boian
    Ahrens, James ORCID
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    Abstract
    Choosing salient time steps from spatio-temporal data is useful for summarizing the sequence and developing visualizations for animations prior to committing time and resources to their production on an entire time series. Animations can be developed more quickly with visualization choices that work best for a small set of the important salient timesteps. Here we introduce a new unsupervised learning method for finding such salient timesteps. The volumetric data is represented by a 4-dimensional non-negative tensor, X(t; x; y; z).The presence of latent (not directly observable) structure in this tensor allows a unique representation and compression of the data. To extract the latent time-features we utilize non-negative Tucker tensor decomposition. We then map these time-features to their maximal values to identify the salient time steps. We demonstrate that this choice of time steps allows a good representation of the time series as a whole.
    BibTeX
    @inproceedings {10.2312:evs.20211055,
    booktitle = {EuroVis 2021 - Short Papers},
    editor = {Agus, Marco and Garth, Christoph and Kerren, Andreas},
    title = {{Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition}},
    author = {Pulido, Jesus and Patchett, John and Bhattarai, Manish and Alexandrov, Boian and Ahrens, James},
    year = {2021},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-143-4},
    DOI = {10.2312/evs.20211055}
    }
    URI
    https://doi.org/10.2312/evs.20211055
    https://diglib.eg.org:443/handle/10.2312/evs20211055
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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