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Multi-Scale Modeling for Stratified Space Data

by

Professor Ellen Gasparovich

January 15, 2016
4:30 pm
Bailey Hall 207

Refreshments in the common room at 4:15


Abstract:

Geometric and topological methods in data analysis are capable of exposing essential shape information that may be hidden in the original data. Our goal is to introduce an algorithm for producing multi-scale models for data using an adaptive cover tree methodology, techniques from persistent homology, and multi-scale local principal component analysis. Our method takes in a point cloud sampled from a stratified space, and produces a model for the data that is fundamentally based on its local geometric properties and that captures how the different pieces of the dataset fit together. We will consider applications of our methods to musical audio data.


For additional information, send e-mail to math@union.edu or call (518) 388-6246.
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