Challenges in Topological Object Data Analysis

Vic Patrangenaru (), Peter Bubenik, Robert L. Paige and Daniel Osborne
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Vic Patrangenaru: Florida State University
Peter Bubenik: University of Florida
Robert L. Paige: Missouri S & T
Daniel Osborne: Florida A&M University

Sankhya A: The Indian Journal of Statistics, 2019, vol. 81, issue 1, No 9, 244-271

Abstract: Abstract Statistical analysis on object data presents many challenges. Basic summaries such as means and variances are difficult to compute. We apply ideas from topology to study object data. We present a framework for using death vectors and persistence landscapes to vectorize object data and perform statistical analysis. We apply this method to some common leaf images that were previously shown to be challenging to compare using a 3D shape techniques. Surprisingly, the most persistent features are shown to be “topological noise” and the statistical analysis depends on the less persistent features which we refer to as the “geometric signal”. We also describe the first steps to a new approach to using topology for object data analysis, which applies topology to distributions on object spaces. We introduce a new Fréchet-Morse function technique for probability distribution on a compact object space, extending the Fréchet means lo a larger number of location parameters, including Fréchet antimeans. An example of 3D data analysis to distinguish two flowers using the new location parameters associated with a Veronese-Whitney (VW) embedding of random projective shapes of 3D configurations extracted from a set of pairs of their digital camera images is also given here.

Keywords: VW-means of index r; Topological data analysis; Relative homology; Object data analysis; Persistence landscapes; Primary: 62H35; 62G10; 55N05; Secondary: 57R45; 55N99; 62G99 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s13171-018-0137-7

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