Overview
- Provides a coherent overview of the emerging field of non-Euclidean similarity learning
- Presents a broad range of perspectives on similarity-based pattern analysis and recognition methods, from purely theoretical challenges to practical, real-world applications
- Includes coverage of both supervised and unsupervised learning paradigms, as well as generative and discriminative models
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)
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About this book
This accessible text/reference presents a coherent overview of the emerging field of non-Euclidean similarity learning. The book presents a broad range of perspectives on similarity-based pattern analysis and recognition methods, from purely theoretical challenges to practical, real-world applications. The coverage includes both supervised and unsupervised learning paradigms, as well as generative and discriminative models. Topics and features: explores the origination and causes of non-Euclidean (dis)similarity measures, and how they influence the performance of traditional classification algorithms; reviews similarity measures for non-vectorial data, considering both a “kernel tailoring” approach and a strategy for learning similarities directly from training data; describes various methods for “structure-preserving” embeddings of structured data; formulates classical pattern recognition problems from a purely game-theoretic perspective; examines two large-scale biomedical imagingapplications.
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Keywords
Table of contents (10 chapters)
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Front Matter
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Foundational Issues
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Front Matter
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Deriving Similarities for Non-vectorial Data
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Front Matter
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Embedding and Beyond
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Front Matter
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Back Matter
Editors and Affiliations
Bibliographic Information
Book Title: Similarity-Based Pattern Analysis and Recognition
Editors: Marcello Pelillo
Series Title: Advances in Computer Vision and Pattern Recognition
DOI: https://doi.org/10.1007/978-1-4471-5628-4
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag London 2013
Hardcover ISBN: 978-1-4471-5627-7Published: 12 December 2013
Softcover ISBN: 978-1-4471-6950-5Published: 17 September 2016
eBook ISBN: 978-1-4471-5628-4Published: 26 November 2013
Series ISSN: 2191-6586
Series E-ISSN: 2191-6594
Edition Number: 1
Number of Pages: XIV, 291
Number of Illustrations: 19 b/w illustrations, 46 illustrations in colour
Topics: Pattern Recognition