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Erschienen in:
The Study of Improving the Accuracy of Convolutional Neural Networks in Face Recognition Tasks Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Learning Topology: Bridging Computational Topology and Machine Learning

verfasst von : Davide Moroni, Maria Antonietta Pascali

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

Topology is a classical branch of mathematics, born essentially from Euler’s studies in the XVII century, which deals with the abstract notion of shape and geometry. Last decades were characterised by a renewed interest in topology and topology-based tools, due to the birth of computational topology and Topological Data Analysis (TDA). A large and novel family of methods and algorithms computing topological features and descriptors (e.g. persistent homology) have proved to be effective tools for the analysis of graphs, 3d objects, 2D images, and even heterogeneous datasets. This survey is intended to be a concise but complete compendium that, offering the essential basic references, allows you to orient yourself among the recent advances in TDA and its applications, with an eye to those related to machine learning and deep learning.

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Vorheriges Kapitel High-Performance Algorithms Application for Retinal Image Segmentation Based on Texture Features
Nächstes Kapitel Library of Sample Image Instances for the Cutting Path Problem
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Metadaten
Titel
Learning Topology: Bridging Computational Topology and Machine Learning
verfasst von
Davide Moroni
Maria Antonietta Pascali
Copyright-Jahr
2021
Buch
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68820-2

Electronic ISBN: 978-3-030-68821-9

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-68821-9_20