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Challenges in Topological Object Data Analysis

Author

Listed:
  • Vic Patrangenaru

    (Florida State University)

  • Peter Bubenik

    (University of Florida)

  • Robert L. Paige

    (Missouri S & T)

  • Daniel Osborne

    (Florida A&M University)

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.

Suggested Citation

  • Vic Patrangenaru & Peter Bubenik & Robert L. Paige & Daniel Osborne, 2019. "Challenges in Topological Object Data Analysis," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 244-271, February.
    • Handle: RePEc:spr:sankha:v:81:y:2019:i:1:d:10.1007_s13171-018-0137-7
    DOI: 10.1007/s13171-018-0137-7
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    References listed on IDEAS

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    1. R. N. Bhattacharya & L. Ellingson & X. Liu & V. Patrangenaru & M. Crane, 2012. "Extrinsic analysis on manifolds is computationally faster than intrinsic analysis with applications to quality control by machine vision," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 28(3), pages 222-235, May.
    2. Crane, M. & Patrangenaru, V., 2011. "Random change on a Lie group and mean glaucomatous projective shape change detection from stereo pair images," Journal of Multivariate Analysis, Elsevier, vol. 102(2), pages 225-237, February.
    3. Ellingson, Leif & Patrangenaru, Vic & Ruymgaart, Frits, 2013. "Nonparametric estimation of means on Hilbert manifolds and extrinsic analysis of mean shapes of contours," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 317-333.
    4. Harman, Radoslav & Lacko, Vladimír, 2010. "On decompositional algorithms for uniform sampling from n-spheres and n-balls," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2297-2304, November.
    5. Victor Patrangenaru & Robert Paige & K. David Yao & Mingfei Qiu & David Lester, 2016. "Projective shape analysis of contours and finite 3D configurations from digital camera images," Statistical Papers, Springer, vol. 57(4), pages 1017-1040, December.
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