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Creators/Authors contains: "Meiss, J.D."

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  1. ; (, Physica D: Nonlinear Phenomena)
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  2. ; (, Physica D: Nonlinear Phenomena)
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  3. ; ; ; ; (, Advances in Intelligent Data Analysis XVI. IDA 2017)
    opological data analysis (TDA), while abstract, allows a characterization of time-series data obtained from nonlinear and complex dynamical systems. Though it is surprising that such an abstract measure of structure—counting pieces and holes—could be useful for real-world data, TDA lets us compare different systems, and even do membership testing or change-point detection. However, TDA is computationally expensive and involves a number of free parameters. This complexity can be obviated by coarse-graining, using a construct called the witness complex. The parametric dependence gives rise to the concept of persistent homology: how shape changes with scale. Its results allow us to distinguish time-series data from different systems—e.g., the same note played on different musical instruments. 
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