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190823 ||| eng |
| 020 |
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|a 9781108297806
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| 050 |
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4 |
|a QA491
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| 100 |
1 |
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|a Boissonnat, J.-D.
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| 245 |
0 |
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|a Geometric and topological inference
|c Jean-Daniel Boissonnat, INRIA Sophia Antipolis, Frédéric Chazal, Inria Saclay-Ile-de-France, Mariette Yvinec, INRIA Sophia Antipolis
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| 260 |
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|a Cambridge
|b Cambridge University Press
|c 2018
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| 300 |
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|a xii, 233 pages0
|b digital
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| 653 |
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|a Shapes / Mathematical models
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| 653 |
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|a Geometric analysis
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| 653 |
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|a Pattern perception
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| 653 |
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|a Topology
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| 700 |
1 |
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|a Chazal, Frédéric
|e [author]
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| 700 |
1 |
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|a Yvinec, Mariette
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b CBO
|a Cambridge Books Online
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| 490 |
0 |
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|a Cambridge texts in applied mathematics
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| 856 |
4 |
0 |
|u https://doi.org/10.1017/9781108297806
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 514.2
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| 520 |
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|a 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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