Geometric and Topological Inference

Nonfiction, Science & Nature, Mathematics, Computers, General Computing, Reference & Language, Reference
Cover of the book Geometric and Topological Inference by Jean-Daniel Boissonnat, Frédéric Chazal, Mariette Yvinec, Cambridge University Press
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Author: Jean-Daniel Boissonnat, Frédéric Chazal, Mariette Yvinec ISBN: 9781108317610
Publisher: Cambridge University Press Publication: August 31, 2018
Imprint: Cambridge University Press Language: English
Author: Jean-Daniel Boissonnat, Frédéric Chazal, Mariette Yvinec
ISBN: 9781108317610
Publisher: Cambridge University Press
Publication: August 31, 2018
Imprint: Cambridge University Press
Language: English

Geometric and topological inference deals with the retrieval of information about a geometric object using only a finite set of possibly noisy sample points. It has connections to manifold learning and provides the mathematical and algorithmic foundations of the rapidly evolving field of topological data analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this self-contained book covers key aspects of the field, from data representation and combinatorial questions to manifold reconstruction and persistent homology. It can serve as a textbook for graduate students or researchers in mathematics, computer science and engineering interested in a geometric approach to data science.

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Geometric and topological inference deals with the retrieval of information about a geometric object using only a finite set of possibly noisy sample points. It has connections to manifold learning and provides the mathematical and algorithmic foundations of the rapidly evolving field of topological data analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this self-contained book covers key aspects of the field, from data representation and combinatorial questions to manifold reconstruction and persistent homology. It can serve as a textbook for graduate students or researchers in mathematics, computer science and engineering interested in a geometric approach to data science.

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