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Advances in Self-Organizing Maps and Learning Vector Quantization

Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014

  • Conference proceedings
  • © 2014

Overview

Editors:
  1. Thomas Villmann

    1. Department of Mathematics, University of Applied Sciences Mittweida, Mittweida, Germany

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  2. Frank-Michael Schleif

    1. University of Applied Sciences Mittweida, Mittweida, Germany

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  3. Marika Kaden

    1. University of Applied Sciences Mittweida, Mittweida, Germany

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  4. Mandy Lange

    1. University of Applied Sciences Mittweida, Mittweida, Germany

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  • Covers newest theoretical developments for self-organizing maps and learning vector quantization
  • Presents computational aspects and excellent applications for data mining and visualization in several application areas
  • Contains refereed papers presented at the Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Germany, on July 2-4, 2014

Part of the book series: Advances in Intelligent Systems and Computing (AISC, volume 295)

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About this book

The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first WSOM-workshop 1997 in Helsinki this workshop focuses on newest results in the field of supervised and unsupervised vector quantization like self-organizing maps for data mining and data classification.

This 10th WSOM brought together more than 50 researchers, experts and practitioners in the beautiful small town Mittweida in Saxony (Germany) nearby the mountains Erzgebirge to discuss new developments in the field of unsupervised self-organizing vector quantization systems and learning vector quantization approaches for classification. The book contains the accepted papers of the workshop after a careful review process as well as summaries of the invited talks. Among these book chapters there are excellent examples of the use of self-organizing maps in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis and time series analysis. Other chapters present the latest theoretical work on self-organizing maps as well as learning vector quantization methods, such as relating those methods to classical statistical decision methods.

All the contribution demonstrate that vector quantization methods cover a large range of application areas including data visualization of high-dimensional complex data, advanced decision making and classification or data clustering and data compression.

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Keywords

Table of contents (29 papers)

  1. Front Matter

    Pages 1-11
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  2. SOM-Theory and Visualization Techniques

    1. Front Matter

      Pages 1-1
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    2. How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?

      • Fabrice Rossi
      Pages 3-23

    3. Dynamic Formation of Self-Organizing Maps

      • Jérémy Fix
      Pages 25-34

    4. MS-SOM: Magnitude Sensitive Self-Organizing Maps

      • Enrique Pelayo, David Buldain
      Pages 35-44

    5. Bagged Kernel SOM

      • Jérôme Mariette, Madalina Olteanu, Julien Boelaert, Nathalie Villa-Vialaneix
      Pages 45-54

    6. Probability Ridges and Distortion Flows: Visualizing Multivariate Time Series Using a Variational Bayesian Manifold Learning Method

      • Alessandra Tosi, Iván Olier, Alfredo Vellido
      Pages 55-64

    7. Short Review of Dimensionality Reduction Methods Based on Stochastic Neighbour Embedding

      • Diego H. Peluffo-Ordóñez, John A. Lee, Michel Verleysen
      Pages 65-74

  3. Prototype Based Classification

    1. Front Matter

      Pages 75-75
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    2. Attention Based Classification Learning in GLVQ and Asymmetric Misclassification Assessment

      • Marika Kaden, W. Hermann, Thomas Villmann
      Pages 77-87

    3. Visualization and Classification of DNA Sequences Using Pareto Learning Self Organizing Maps Based on Frequency and Correlation Coefficient

      • Hiroshi Dozono
      Pages 89-98

    4. Probabilistic Prototype Classification Using t-norms

      • Tina Geweniger, Frank-Michael Schleif, Thomas Villmann
      Pages 99-108

    5. Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches

      • Lydia Fischer, David Nebel, Thomas Villmann, Barbara Hammer, Heiko Wersing
      Pages 109-118

  4. Classification and Non-Standard Metrics

    1. Front Matter

      Pages 119-119
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    2. Prototype-Based Classifiers and Their Application in the Life Sciences

      • Michael Biehl
      Pages 121-121

    3. Generative versus Discriminative Prototype Based Classification

      • Barbara Hammer, David Nebel, Martin Riedel, Thomas Villmann
      Pages 123-132

    4. Some Room for GLVQ: Semantic Labeling of Occupancy Grid Maps

      • Sven Hellbach, Marian Himstedt, Frank Bahrmann, Martin Riedel, Thomas Villmann, Hans-Joachim Böhme
      Pages 133-143

    5. Anomaly Detection Based on Confidence Intervals Using SOM with an Application to Health Monitoring

      • Anastasios Bellas, Charles Bouveyron, Marie Cottrell, Jerome Lacaille
      Pages 145-155

    6. RFSOM – Extending Self-Organizing Feature Maps with Adaptive Metrics to Combine Spatial and Textural Features for Body Pose Estimation

      • Mathias Klingner, Sven Hellbach, Martin Riedel, Marika Kaden, Thomas Villmann, Hans-Joachim Böhme
      Pages 157-166

    7. Beyond Standard Metrics – On the Selection and Combination of Distance Metrics for an Improved Classification of Hyperspectral Data

      • Uwe Knauer, Andreas Backhaus, Udo Seiffert
      Pages 167-177
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Editors and Affiliations

  • Department of Mathematics, University of Applied Sciences Mittweida, Mittweida, Germany

    Thomas Villmann

  • University of Applied Sciences Mittweida, Mittweida, Germany

    Frank-Michael Schleif, Marika Kaden, Mandy Lange

Bibliographic Information

  • Book Title: Advances in Self-Organizing Maps and Learning Vector Quantization

  • Book Subtitle: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014

  • Editors: Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange

  • Series Title: Advances in Intelligent Systems and Computing

  • DOI: https://doi.org/10.1007/978-3-319-07695-9

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing Switzerland 2014

  • Softcover ISBN: 978-3-319-07694-2Published: 26 June 2014

  • eBook ISBN: 978-3-319-07695-9Published: 10 June 2014

  • Series ISSN: 2194-5357

  • Series E-ISSN: 2194-5365

  • Edition Number: 1

  • Number of Pages: XII, 314

  • Number of Illustrations: 33 b/w illustrations, 81 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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