Pulido, JesusPatchett, JohnBhattarai, ManishAlexandrov, BoianAhrens, JamesAgus, Marco and Garth, Christoph and Kerren, Andreas2021-06-122021-06-122021978-3-03868-143-4https://doi.org/10.2312/evs.20211055https://diglib.eg.org:443/handle/10.2312/evs20211055Choosing 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.Humancentered computingVisualization design and evaluation methods10.2312/evs.2021105555-59