Similarity-Based Pattern Analysis and Recognition

ISBN-13: 9781447169505 / Angielski / Miękka / 2016 / 291 str.

Marcello Pelillo

Opis

The pattern recognition and machine learning communities have, until recently, focused mainly on feature-vector representations, typically considering objects in isolation. However, this paradigm is being increasingly challenged by similarity-based approaches, which recognize the importance of relational and similarity information.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 that provide assistance in the diagnosis of physical and mental illnesses from tissue microarray images and MRI images.This pioneering work is essential reading for graduate students and researchers seeking an introduction to this important and diverse subject.

Spis treści

Introduction: The SIMBAD Project
Marcello Pelillo

Part I: Foundational Issues

Non-Euclidean Dissimilarities: Causes, Embedding and Informativeness
Robert P. W. Duin, Elżbieta Pękalska, and Marco Loog

SIMBAD: Emergence of Pattern Similarity
Joachim M. Buhmann

Part II: Deriving Similarities for Non-vectorial Data

On the Combination of Information Theoretic Kernels with Generative Embeddings
Pedro M. Q. Aguiar, Manuele Bicego, Umberto Castellani, Mário A. T. Figueiredo, André T. Martins, Vittorio Murino, Alessandro Perina, and Aydın Ulaş

Learning Similarities from Examples under the Evidence Accumulation Clustering Paradigm
Ana L. N. Fred, André Lourenço, Helena Aidos, Samuel Rota Bulò, Nicola Rebagliati, Mário Figueiredo, and Marcello Pelillo 

Part III: Embedding and Beyond

Geometricity and Embedding
Peng Ren, Furqan Aziz, Lin Han, Eliza Xu, Richard C. Wilson, and Edwin R. Hancock

Structure Preserving Embedding of Dissimilarity Data
Volker Roth, Thomas J. Fuchs, Julia E. Vogt, Sandhya Prabhakaran, and Joachim M. Buhmann

A Game-Theoretic Approach to Pairwise Clustering and Matching
Marcello Pelillo, Samuel Rota Bulò, Andrea Torsello, Andrea Albarelli, and Emanuele Rodolà

Part IV: Applications

Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma
Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth, and Joachim M. Buhmann

Analysis of Brain Magnetic Resonance (MR) Scans for the Diagnosis of Mental Illness
Aydın Ulaş, Umberto Castellani, Manuele Bicego, Vittorio Murino, Marcella Bellani, Michele Tansella, and Paolo Brambilla

Blurb

The pattern recognition and machine learning communities have, until recently, focused mainly on feature-vector representations, typically considering objects in isolation. However, this paradigm is being increasingly challenged by similarity-based approaches, which recognize the importance of relational and similarity information.

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 that provide assistance in the diagnosis of physical and mental illnesses from tissue microarray images and MRI images

This pioneering work is essential reading for graduate students and researchers seeking an introduction to this important and diverse subject.

Marcello Pelillo is a Full Professor of Computer Science at the University of Venice, Italy. He is a Fellow of the IEEE and of the IAPR.