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Dimensionality Reduction for Information Geometric Characterization of Surface Topographies

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review


Original languageEnglish
Title of host publicationComputational Information Geometry: For Image and Signal Processing
EditorsFrank Nielsen, Frank Critchley, Christopher T. J. Dodson
Place of PublicationCham
Number of pages15
ISBN (Electronic)978-3-319-47058-0
ISBN (Print)978-3-319-47056-6
Publication statusPublished - 2017
Publication typeA3 Part of a book or another research book


Stochastic textures with features spanning many length scales arise in a range of contexts in physical and natural sciences, from nanostructures like synthetic bone to ocean wave height distributions and cosmic phenomena like inter-galactic cluster void distributions. Here we used a data set of 35 surface topographies, each of 2400×2400 pixels with spatial resolution between 4 and 7 μm per pixel, and fitted trivariate Gaussian distributions to represent their spatial structures. For these we computed pairwise information metric distances using the Fisher-Rao metric. Then dimensionality reduction was used to reveal the groupings among subsets of samples in an easily comprehended graphic in 3-space. The samples here came from the papermaking industry but such a reduction of large frequently noisy spatial data sets is useful in a range of materials and contexts at all scales.

Publication forum classification

Field of science, Statistics Finland