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Learning in the context of very high dimensional data (Dagstuhl Seminar 11341)
Authors and all authors of the abstracts in this report
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Dagstuhl Reports, Volume 1, Issue 8
Volume: Dagstuhl Reports, Volume 1
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license
- Publication Date: 2011-12-06
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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)
https://doi.org/10.4230/DagRep.1.8.67
https://doi.org/10.4230/DagRep.1.8.67
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.
Keywords
- Curse of dimensionality
- Dimensionality reduction
- Regularization Deep learning
- Visualization
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