Topological Analysis of Variance and the Maxillary Complex
Giseon Heo, Mathematical and Statistical Sciences, University of Alberta (May 25, 2012)
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Abstract
Persistent homology, a recent development in computational topology, has shown to be useful for analyzing high dimensional non-linear data. In this talk, we connect computational topology with the traditional analysis of variance and demonstrate this synergy on a three-dimensional orthodontic landmark data set derived from the maxillary complex. (Joint work with Jennifer Gamble and Peter Kim)
