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Categorical Dimensions of Human Odor Descriptor Space Revealed by Non-Negative Matrix Factorization

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  • 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.

Suggested Citation

  • Jason B Castro & Arvind Ramanathan & Chakra S Chennubhotla, 2013. "Categorical Dimensions of Human Odor Descriptor Space Revealed by Non-Negative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
    • Handle: RePEc:plo:pone00:0073289
    DOI: 10.1371/journal.pone.0073289
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Berry, Michael W. & Browne, Murray & Langville, Amy N. & Pauca, V. Paul & Plemmons, Robert J., 2007. "Algorithms and applications for approximate nonnegative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 155-173, September.
    3. Jörn Niessing & Rainer W. Friedrich, 2010. "Olfactory pattern classification by discrete neuronal network states," Nature, Nature, vol. 465(7294), pages 47-52, May.
    4. Cornelia I. Bargmann, 2006. "Comparative chemosensation from receptors to ecology," Nature, Nature, vol. 444(7117), pages 295-301, November.
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    Cited by:

    1. Jisub Bae & Ju-Yeon Yi & Cheil Moon, 2019. "Odor quality profile is partially influenced by verbal cues," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-17, December.
    2. María Luisa Delgado-Losada & Jaime Bouhaben & Claudia Ruiz-Huerta & Marcelle V. Canto & Alice Helena Delgado-Lima, 2022. "Long-Lasting Olfactory Dysfunction in Hospital Workers Due to COVID-19: Prevalence, Clinical Characteristics, and Most Affected Odorants," IJERPH, MDPI, vol. 19(9), pages 1-18, May.
    3. Lyu, Minghui & Huang, Qi, 2024. "Visual elements in advertising enhance odor perception and purchase intention: The role of mental imagery in multi-sensory marketing," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).

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