Structural, Syntactic, and Statistical Pattern Recognition

1. Front Matter

   PDF

2. Graph Kernels

   1. A Graph Kernel from the Depth-Based Representation

      • Lu Bai, Peng Ren, Xiao Bai, Edwin R. Hancock

      Pages 1-11

   2. Incorporating Molecule’s Stereisomerism within the Machine Learning Framework

      • Pierre-Anthony Grenier, Luc Brun, Didier Villemin

      Pages 12-21

   3. Transitive State Alignment for the Quantum Jensen-Shannon Kernel

      • Andrea Torsello, Andrea Gasparetto, Luca Rossi, Lu Bai, Edwin R. Hancock

      Pages 22-31

3. Clustering

   1. Balanced K-Means for Clustering

      • Mikko I. Malinen, Pasi Fränti

      Pages 32-41

   2. Poisoning Complete-Linkage Hierarchical Clustering

      • Battista Biggio, Samuel Rota Bulò, Ignazio Pillai, Michele Mura, Eyasu Zemene Mequanint, Marcello Pelillo et al.

      Pages 42-52

   3. A Comparison of Categorical Attribute Data Clustering Methods

      • Ville Hautamäki, Antti Pöllänen, Tomi Kinnunen, Kong Aik Lee, Haizhou Li, Pasi Fränti

      Pages 53-62

4. Graph Edit Distance

   1. Improving Approximate Graph Edit Distance Using Genetic Algorithms

      • Kaspar Riesen, Andreas Fischer, Horst Bunke

      Pages 63-72

   2. Approximate Graph Edit Distance Guided by Bipartite Matching of Bags of Walks

      • Benoit Gaüzère, Sébastien Bougleux, Kaspar Riesen, Luc Brun

      Pages 73-82

   3. A Hausdorff Heuristic for Efficient Computation of Graph Edit Distance

      • Andreas Fischer, Réjean Plamondon, Yvon Savaria, Kaspar Riesen, Horst Bunke

      Pages 83-92

5. Graph Models and Embedding

   1. Flip-Flop Sublinear Models for Graphs

      • Brijnesh Jain

      Pages 93-102

   2. Node Centrality for Continuous-Time Quantum Walks

      • Luca Rossi, Andrea Torsello, Edwin R. Hancock

      Pages 103-112

   3. Max-Correlation Embedding Computation

      • Antonio Robles-Kelly

      Pages 113-122

6. Discriminant Analysis

   1. Fast Gradient Computation for Learning with Tensor Product Kernels and Sparse Training Labels

      • Tapio Pahikkala

      Pages 123-132

   2. Nonlinear Discriminant Analysis Based on Probability Estimation by Gaussian Mixture Model

      • Akinori Hidaka, Takio Kurita

      Pages 133-142

7. Combining and Selecting

   1. Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood

      • Konstantinos Sechidis, Nikolaos Nikolaou, Gavin Brown

      Pages 143-152

   2. Majority Vote of Diverse Classifiers for Late Fusion

      • Emilie Morvant, Amaury Habrard, Stéphane Ayache

      Pages 153-162

8. Joint Session

   1. Entropic Graph Embedding via Multivariate Degree Distributions

      • Cheng Ye, Richard C. Wilson, Edwin R. Hancock

      Pages 163-172

   2. On Parallel Lines in Noisy Forms

      • George Nagy

      Pages 173-182

9. Metrics and Dissimilarities

   1. Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance

      • Robert P. W. Duin, Manuele Bicego, Mauricio Orozco-Alzate, Sang-Woon Kim, Marco Loog

      Pages 183-192

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2014; comprising the International Workshop on Structural and Syntactic Pattern Recognition, SSPR, and the International Workshop on Statistical Techniques in Pattern Recognition, SPR. The total of 25 full papers and 22 poster papers included in this book were carefully reviewed and selected from 78 submissions. They are organized in topical sections named: graph kernels; clustering; graph edit distance; graph models and embedding; discriminant analysis; combining and selecting; joint session; metrics and dissimilarities; applications; partial supervision; and poster session.

• complex network
• genetic algorithms
• graph theory
• kernel methods
• modeling
• multimedia analysis
• neural networks
• object detection
• pattern recognition
• perceptron learning
• random walks
• ranking
• algorithm analysis and problem complexity

• School of Computing, University of Eastern Finland, Joensuu, Finland

  Pasi Fränti

• School of Computer Science, The University of Manchester, Manchester, UK

  Gavin Brown

• Delft University of Technology, Delft, The Netherlands

  Marco Loog

• Universidad de Alicante, Spain

  Francisco Escolano

• Università Ca’ Foscari Venezia, Venezia Mestre, Italy

  Marcello Pelillo

Policies and ethics