Quantifying olfactory perception: mapping olfactory perception space by using multidimensional scaling and self-organizing maps
Autor: | James M. Bower, Ulrich G. Hofmann, Amir Madany Mamlouk, Christine Chee-Ruiter |
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Rok vydání: | 2003 |
Předmět: |
Self-organizing map
business.industry Euclidean space Cognitive Neuroscience Olfaction Space (commercial competition) Machine learning computer.software_genre Computer Science Applications Artificial Intelligence Metric (mathematics) Euclidean geometry Point (geometry) Artificial intelligence Multidimensional scaling business computer Mathematics |
Zdroj: | Neurocomputing. :591-597 |
ISSN: | 0925-2312 |
DOI: | 10.1016/s0925-2312(02)00805-6 |
Popis: | In this paper we describe an effort to project an olfactory perception database onto the nearest high dimensional Euclidean space using multidimensional scaling. This yields an independent Euclidean interpretation of odor perception, whether this space is metric or not. Self-organizing maps were then applied to produce two-dimensional maps of the Euclidean approximation of olfactory perception space. These maps provide new knowledge about complexity and potentially the functionality of the sense of smell from the point of view of human odor perception. This report is based on a recent thesis by Madany Mamlouk, Quantifying olfactory perception, at the University of Lubeck, Germany. |
Databáze: | OpenAIRE |
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