Optimal Mass Transport: Signal processing and machine-learning applications.

Journal: IEEE signal processing magazine
Published Date: Jul 11, 2017

Abstract

Transport-based techniques for signal and data analysis have received increased attention recently. Given their ability to provide accurate generative models for signal intensities and other data distributions, they have been used in a variety of applications including content-based retrieval, cancer detection, image super-resolution, and statistical machine learning, to name a few, and shown to produce state of the art results in several applications. Moreover, the geometric characteristics of transport-related metrics have inspired new kinds of algorithms for interpreting the meaning of data distributions. Here we provide a practical overview of the mathematical underpinnings of mass transport-related methods, including numerical implementation, as well as a review, with demonstrations, of several applications. Software accompanying this tutorial is available at [43].

Authors

  • Soheil Kolouri
    Department of Computer Science, Vanderbilt University, Nashville, TN 37212, USA.
  • Serim Park
  • Matthew Thorpe
    Department of Radiology, Division of Nuclear Medicine Mayo Clinic Rochester Minnesota USA.
  • Dejan Slepčev
  • Gustavo K Rohde
    Imaging and Data Science Laboratory Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Virginia.

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