Categorical dimensions of human odor descriptor space revealed by non-negative matrix factorization
- University of Pittsburgh School of Medicine, Pittsburgh PA
- Bates College
In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain un- clear. Here, we use non-negative matrix factorization (NMF) - a dimensionality reduction technique - to uncover structure in a panel of odor profiles, with each odor defined as a point in multi-dimensional descriptor space. The properties of NMF are favorable for the analysis of such lexical and perceptual data, and lead to a high-dimensional account of odor space. We further provide evidence that odor di- mensions apply categorically. That is, odor space is not occupied homogenously, but rather in a discrete and intrinsically clustered manner. We discuss the potential implications of these results for the neural coding of odors, as well as for developing classifiers on larger datasets that may be useful for predicting perceptual qualities from chemical structures.
- Research Organization:
- Oak Ridge National Laboratory (ORNL)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1107833
- Journal Information:
- PLoS ONE, Journal Name: PLoS ONE Journal Issue: 9 Vol. 8
- Country of Publication:
- United States
- Language:
- English
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