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
