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Abstract: Observations from real-world problems are often highdimensional vectors, i.e. made up of many variables. Learning methods, including artificial neural networks, often have difficulties to handle a relatively small number of high-dimensional data. In this paper, we show how concepts gained from our intuition on 2- and 3dimensional data can be misleading when used in high-dimensional settings. When then show how the "curse of dimensionality" and the "empty space phenomenon" can be taken... (Update)
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BibTeX entry: (Update)
Verleysen, M.: Learning high-dimensional data. Acc. for public. in Ablameyko, S., Goras, L., Gori, M., Piuri, V. (eds): Limitations and future trends in neural computation, IOS Press. http://citeseer.ist.psu.edu/554216.html More
@misc{ verleysen-learning,
author = "M. Verleysen",
title = "Learning high-dimensional data",
text = "Verleysen, M.: Learning high-dimensional data. Acc. for public. in Ablameyko,
S., Goras, L., Gori, M., Piuri, V. (eds): Limitations and future trends
in neural computation, IOS Press.",
url = "citeseer.ist.psu.edu/554216.html" }
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