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A coherent framework for learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data

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Academic year: 2021

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Figure 1: The generic piecewise geodesic curve model. The observed data (crosses) consist of noisy samples along manifold- manifold-valued trajectories
Figure 2: The piecewise logistic curve model. Figure 2a: A typical representative trajectory in bold and several individual ones, for different vectors z i
Figure 3: The piecewise geodesic shape model: Construction of the group-representative trajectory
Table 2: The piecewise logistic curve model – Fixed effects. Mean (standard deviation) relative errors (expressed as a percentage) over 50 runs for the estimated population parameters z pop estim according to the data set and the sample size n.
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