Frank (Hrsg.) Nielsen

Geometric Structures of Information

Buch

This book focuses on information geometry manifolds of structured data/information and their advanced applications featuring new and fruitful interactions between several branches of science: information science, mathematics and physics. It addresses interrelations between different mathematical domains like shape spaces, probability/optimization & algorithms on manifolds, relational and discrete metric spaces, computational and Hessian information geometry, algebraic/infinite dimensional/Banach information manifolds, divergence geometry, tensor-valued morphology, optimal transport theory, manifold & topology learning, and application… Mehr

CHF 197.00

Preise inkl. MwSt. und Versandkosten (Portofrei ab CHF 40.00)

Versandfertig innerhalb 1-3 Werktagen

V106: Fremdlagertitel. Lieferzeit unbestimmt

Produktdetails

ISBN: 978-3-030-02519-9
EAN: 9783030025199
Produktnummer: 28797653
Verlag: Springer International Publishing
Sprache: Englisch
Erscheinungsjahr: 2018
Seitenangabe: 400 S.
Masse: H24.1 cm x B16.0 cm x D2.7 cm 764 g
Auflage: 1st ed. 2019
Abbildungen: HC runder Rücken kaschiert
Gewicht: 764

Über den Autor

Frank Nielsen is Professor at the Laboratoire d'informatique de l'École polytechnique, Paris, France. His research aims at understanding the nature and structure of information and randomness in data, and exploiting algorithmically this knowledge in innovative imaging applications. For that purpose, he coined the field of computational information geometry (computational differential geometry) to extract information as regular structures whilst taking into account variability in datasets by grounding them in geometric spaces. Geometry beyond Euclidean spaces has a long history of revolutionizing the way we perceived reality. Curved spacetime geometry, sustained relativity theory and fractal geometry unveiled the scale-free properties of Nature. In the digital world, geometry is data-driven and allows intrinsic data analytics by capturing the very essence of data through invariance principles without being biased by such or such particular data representation.