Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#329938] of Francis C. Motta

Papers Published

  1. Motta, FC, Topological Data Analysis: Developments and Applications, in Advances in Nonlinear Geosciences, edited by Tsonis, A (November, 2017), pp. 369-391, Springer, ISBN 3319588958
    (last updated on 2018/08/05)

    Topological Data Analysis (TDA) and its mainstay computational device, persistent homology (PH), has established a strong track record of providing researchers across the data-driven sciences with new insights and methodologies by characterizing low-dimensional geometric structures in high-dimensional data. When combined with machine learning (ML) methods, PH is valued as a discriminating-feature extraction tool. This work highlights many of the recent successes at the intersection of TDA and ML, introduces some of the foundational mathematics underpinning TDA, and summarizes the efforts to strengthen the bridge between TDA and ML. Thus, this document is a launching point for experimentalists and theoreticians to consider what can be learned from the shape of their data.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320