Overview
- Features new and fruitful interactions between several branches of science: information science, mathematics and physics
- Offers a substantial information source for industry and academia
- Collects the most important contributions to the conference GSI’2019 – Geometric Science of Information
Part of the book series: Signals and Communication Technology (SCT)
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About this book
This book focuses on information-geometric manifolds of structured data and models and related applied mathematics. It features new and fruitful interactions between several branches of science: Advanced Signal/Image/Video Processing, Complex Data Modeling and Analysis, Statistics on Manifolds, Topology/Machine/Deep Learning and Artificial Intelligence. The selection of applications makes the book a substantial information source, not only for academic scientist but it is also highly relevant for industry.
The book project was initiated following discussions at the international conference GSI’2019 – Geometric Science of Information that was held at ENAC, Toulouse (France).
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Keywords
Table of contents (10 chapters)
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Front Matter
Editors and Affiliations
About the editor
Frank Nielsen is Senior Researcher at Sony Computer Science Laboratories Inc, Tokyo, Japan and a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS). He taught at Ecole Polytechnique (Palaiseau, France) visual computing and high performance computing (HPC) for data science. His research aims at understanding the nature and structure of information and variability in data and exploiting algorithmically this knowledge in innovative imaging and machine learning applications. For that purpose, he coined the field of computational information geometry (computational differential geometry) to extract information as regular structures while 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 any particular data representation. He is an editor of the journal Entropy (MDPI) and of the journal Information Geometry (INGE, Springer), and co-organize the biannual internation conference on the Geometric Sciences of Information (GSI).
Bibliographic Information
Book Title: Progress in Information Geometry
Book Subtitle: Theory and Applications
Editors: Frank Nielsen
Series Title: Signals and Communication Technology
DOI: https://doi.org/10.1007/978-3-030-65459-7
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-65458-0Published: 15 March 2021
Softcover ISBN: 978-3-030-65461-0Published: 16 March 2022
eBook ISBN: 978-3-030-65459-7Published: 14 March 2021
Series ISSN: 1860-4862
Series E-ISSN: 1860-4870
Edition Number: 1
Number of Pages: XII, 274
Number of Illustrations: 70 b/w illustrations, 35 illustrations in colour
Topics: Data-driven Science, Modeling and Theory Building, Global Analysis and Analysis on Manifolds, Communications Engineering, Networks, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Signal, Image and Speech Processing, Coding and Information Theory