Quantifying olfactory perception: Mapping olfactory perception space by using multidimensional scaling and self-organizing maps

Amir Madany Mamlouk, Christine Chee-Ruiter, Ulrich G. Hofmann, James M. Bower

Research output: Contribution to journalArticle

41 Scopus citations

Abstract

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 Lübeck, Germany.

Original languageEnglish (US)
Pages (from-to)591-597
Number of pages7
JournalNeurocomputing
Volume52-54
DOIs
StatePublished - Jun 1 2003

Keywords

  • Multidimensional scaling
  • Olfactory perception
  • Self-organizing maps

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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