Title |
Categorical Dimensions of Human Odor Descriptor Space Revealed by Non-Negative Matrix Factorization
|
---|---|
Published in |
PLOS ONE, September 2013
|
DOI | 10.1371/journal.pone.0073289 |
Pubmed ID | |
Authors |
Jason B. Castro, Arvind Ramanathan, Chakra S. Chennubhotla |
Abstract |
In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain unclear. 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 dimensions 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. |
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Country | Count | As % |
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United Kingdom | 5 | 14% |
Italy | 2 | 6% |
Chile | 1 | 3% |
Canada | 1 | 3% |
Netherlands | 1 | 3% |
Mexico | 1 | 3% |
Uruguay | 1 | 3% |
Australia | 1 | 3% |
Other | 2 | 6% |
Unknown | 14 | 39% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 28 | 78% |
Scientists | 6 | 17% |
Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 4 | 2% |
Netherlands | 2 | <1% |
United States | 2 | <1% |
Brazil | 1 | <1% |
Denmark | 1 | <1% |
France | 1 | <1% |
Japan | 1 | <1% |
Korea, Republic of | 1 | <1% |
Greece | 1 | <1% |
Other | 1 | <1% |
Unknown | 192 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 45 | 22% |
Student > Ph. D. Student | 34 | 16% |
Student > Master | 28 | 14% |
Student > Doctoral Student | 12 | 6% |
Professor > Associate Professor | 11 | 5% |
Other | 42 | 20% |
Unknown | 35 | 17% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 39 | 19% |
Computer Science | 22 | 11% |
Psychology | 15 | 7% |
Medicine and Dentistry | 13 | 6% |
Engineering | 13 | 6% |
Other | 60 | 29% |
Unknown | 45 | 22% |