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Learning in the context of very high dimensional data (Dagstuhl Seminar 11341)

Authors: Michael Biehl, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, and Thomas Villman

Published in: Dagstuhl Reports, Volume 1, Issue 8 (2011)

Abstract

This report documents the program and the outcomes of Dagstuhl Seminar 11341 "Learning in the context of very high dimensional data". The aim of the seminar was to bring together researchers who develop, investigate, or apply machine learning methods for very high dimensional data to advance this important field of research. The focus was be on broadly applicable methods and processing pipelines, which offer efficient solutions for high-dimensional data analysis appropriate for a wide range of application scenarios.

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Michael Biehl, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, and Thomas Villman. Learning in the context of very high dimensional data (Dagstuhl Seminar 11341). In Dagstuhl Reports, Volume 1, Issue 8, pp. 67-95, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@Article{biehl_et_al:DagRep.1.8.67,
  author =	{Biehl, Michael and Hammer, Barbara and Mer\'{e}nyi, Erzs\'{e}bet and Sperduti, Alessandro and Villman, Thomas},
  title =	{{Learning in the context of very high dimensional data (Dagstuhl Seminar 11341)}},
  pages =	{67--95},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2011},
  volume =	{1},
  number =	{8},
  editor =	{Biehl, Michael and Hammer, Barbara and Mer\'{e}nyi, Erzs\'{e}bet and Sperduti, Alessandro and Villman, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.1.8.67},
  URN =		{urn:nbn:de:0030-drops-33125},
  doi =		{10.4230/DagRep.1.8.67},
  annote =	{Keywords: Curse of dimensionality, Dimensionality reduction, Regularization Deep learning, Visualization}
}

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DOI: 10.4230/DagSemProc.07161.2

A general framework for unsupervised preocessing of structured data

Authors: Barbara Hammer, Alessio Micheli, and Alessandro Sperduti

Published in: Dagstuhl Seminar Proceedings, Volume 7161, Probabilistic, Logical and Relational Learning - A Further Synthesis (2008)

Abstract

We propose a general framework for unsupervised recurrent and recursive networks. This proposal covers various popular approaches like standard self organizing maps (SOM), temporal Kohonen maps, resursive SOM, and SOM for structured data. We define Hebbian learning within this general framework. We show how approaches based on an energy function, like neural gas, can be transferred to this abstract framework so that proposals for new learning algorithms emerge.

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Barbara Hammer, Alessio Micheli, and Alessandro Sperduti. A general framework for unsupervised preocessing of structured data. In Probabilistic, Logical and Relational Learning - A Further Synthesis. Dagstuhl Seminar Proceedings, Volume 7161, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{hammer_et_al:DagSemProc.07161.2,
  author =	{Hammer, Barbara and Micheli, Alessio and Sperduti, Alessandro},
  title =	{{A general framework for unsupervised preocessing of structured data}},
  booktitle =	{Probabilistic, Logical and Relational Learning - A Further Synthesis},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{7161},
  editor =	{Luc de Raedt and Thomas Dietterich and Lise Getoor and Kristian Kersting and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07161.2},
  URN =		{urn:nbn:de:0030-drops-13837},
  doi =		{10.4230/DagSemProc.07161.2},
  annote =	{Keywords: Relational clustering, median clustering, recursive SOM models, kernel SOM}
}

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Documents authored by Sperduti, Alessandro

Learning in the context of very high dimensional data (Dagstuhl Seminar 11341)

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A general framework for unsupervised preocessing of structured data

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