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Friday, October 19
Fri, Oct 19, 7:30 AM - 8:30 AM
Hall of Mirrors
Continental Breakfast and Speed Poster 2, Sponsored by Fifth Third Bank

Analysis of Signal Classification via Persistent Homology (304950)

*Cassie Putman Micucci, University of Tennessee 
Vasileios Maroulas, University of Tennessee 

Keywords: Classification of Time Series, Data Space of Persistence Diagrams, Wasserstein Metric, Cardinality, Persistent homology

Classification of signals is a well-known problem within machine learning, although many methods overlook significant geometric or topological structure in the data. To investigate these features, we create a persistence diagram after transforming a signal into a point cloud using Takens’ embedding. We then consider a new metric on the space of persistence diagrams that accounts both for the matching of points and for the cardinality of the diagrams. This metric generates a classification algorithm for the signals. We also investigate the stability properties of this metric; this analysis provides justification for the use of the metric for comparisons of such diagrams.