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Structural, Syntactic, and Statistical Pattern Recognition

Joint IAPR International Workshops, S+SSPR 2020, Padua, Italy, January 21–22, 2021, Proceedings

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Andrea Torsello

    1. Ca’ Foscari University of Venice, Venice, Italy

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  2. Luca Rossi

    1. Queen Mary University of London, London, UK

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  3. Marcello Pelillo

    1. Università Ca' Foscari Venezia, Venice, Italy

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  4. Battista Biggio

    1. University of Cagliari, Cagliari, Italy

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  5. Antonio Robles-Kelly

    1. Deakin University, Burwood, Australia

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12644)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

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Table of contents (35 papers)

  1. Front Matter

    Pages i-xii
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  2. Classification and Data Processing

    1. Front Matter

      Pages 1-1
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    2. Target Robust Discriminant Analysis

      • Wouter M. Kouw, Marco Loog
      Pages 3-13

    3. Complex-Valued Embeddings of Generic Proximity Data

      • Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif
      Pages 14-23

    4. Metric Learning for Multi-label Classification

      • Marco Brighi, Annalisa Franco, Dario Maio
      Pages 24-33

    5. An Alternative Exploitation of Isolation Forests for Outlier Detection

      • Antonella Mensi, Alessio Franzoni, David M. J. Tax, Manuele Bicego
      Pages 34-44

    6. Exponential Weighted Moving Average of Time Series in Arbitrary Spaces with Application to Strings

      • Alexander Welsing, Andreas Nienkötter, Xiaoyi Jiang
      Pages 45-54

    7. Experimental Analysis of Bidirectional Pairwise Ordinal Classifier Cascades

      • Peter Bellmann, Ludwig Lausser, Hans A. Kestler, Friedhelm Schwenker
      Pages 55-64

  3. Deep Learning

    1. Front Matter

      Pages 65-65
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    2. On Calibration of Mixup Training for Deep Neural Networks

      • Juan Maroñas, Daniel Ramos, Roberto Paredes
      Pages 67-76

    3. Augmenting Graph Convolutional Neural Networks with Highpass Filters

      • Fatemeh Ansarizadeh, David B. Tay, Dhananjay Thiruvady, Antonio Robles-Kelly
      Pages 77-86

    4. Selecting Features from Time Series Using Attention-Based Recurrent Neural Networks

      • Michal Myller, Michal Kawulok, Jakub Nalepa
      Pages 87-97

    5. Feature Extraction Functions for Neural Logic Rule Learning

      • Shashank Gupta, Antonio Robles-Kelly, Mohamed Reda Bouadjenek
      Pages 98-107

    6. Learning High-Resolution Domain-Specific Representations with a GAN Generator

      • Danil Galeev, Konstantin Sofiiuk, Danila Rukhovich, Mikhail Romanov, Olga Barinova, Anton Konushin
      Pages 108-118

    7. Predicting Polypharmacy Side Effects Through a Relation-Wise Graph Attention Network

      • Vincenzo Carletti, Pasquale Foggia, Antonio Greco, Antonio Roberto, Mario Vento
      Pages 119-128

    8. LGL-GNN: Learning Global and Local Information for Graph Neural Networks

      • Huan Li, Boyuan Wang, Lixin Cui, Lu Bai, Edwin R. Hancock
      Pages 129-138

    9. Graph Transformer: Learning Better Representations for Graph Neural Networks

      • Boyuan Wang, Lixin Cui, Lu Bai, Edwin R. Hancock
      Pages 139-149

  4. Graph-Theoretic Methods

    1. Front Matter

      Pages 151-151
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    2. Weighted Network Analysis Using the Debye Model

      • Haoran Zhu, Hui Wu, Jianjia Wang, Edwin R. Hancock
      Pages 153-163

    3. Estimating the Manifold Dimension of a Complex Network Using Weyl’s Law

      • Luca Rossi, Andrea Torsello
      Pages 164-173
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  1. Structural, Syntactic, and Statistical Pattern Recognition

Keywords

About this book

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2020, held in Padua, Italy, in January 2021.

The 35 papers presented in this volume were carefully reviewed and selected from 81 submissions.

The accepted papers cover the major topics of current interest in pattern recognition, including classification and clustering, deep learning, structural matching and graph-theoretic methods, and multimedia analysis and understanding.

Editors and Affiliations

  • Ca’ Foscari University of Venice, Venice, Italy

    Andrea Torsello

  • Queen Mary University of London, London, UK

    Luca Rossi

  • Università Ca' Foscari Venezia, Venice, Italy

    Marcello Pelillo

  • University of Cagliari, Cagliari, Italy

    Battista Biggio

  • Deakin University, Burwood, Australia

    Antonio Robles-Kelly

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