Similarity-Based Pattern Analysis and Recognition, 1st Edition

  • Published By:
  • ISBN-10: 1447156285
  • ISBN-13: 9781447156284
  • DDC: 006.37
  • Grade Level Range: College Freshman - College Senior
  • 291 Pages | eBook
  • Original Copyright 2013 | Published/Released June 2014
  • This publication's content originally published in print form: 2013

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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 imaging applications.

Table of Contents

Front Cover.
Half Title.
Title Page.
Copyright Page.
Other Frontmatter.
1: Introduction: The SIMBAD Project.
2: Foundational Issues.
3: Non-Euclidean Dissimilarities: Causes, Embedding and Informativeness.
4: SIMBAD: Emergence of Pattern Similarity.
5: Deriving Similarities for Non-Vectorial Data.
6: On the Combination of Information-Theoretic Kernels with Generative Embeddings.
7: Learning Similarities from Examples under the Evidence Accumulation Clustering Paradigm.
8: Embedding and Beyond.
9: Geometricity and Embedding.
10: Structure Preserving Embedding of Dissimilarity Data.
11: A Game-Theoretic Approach to Pairwise Clustering and Matching.
12: Applications.
13: Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma.
14: Analysis of Brain Magnetic Resonance (MR) Scans for the Diagnosis of Mental Illness.