Bayesian Probability Approach to Feature Significance for Infrared Spectra of Bacteria

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The significance of a spectral feature is defined as the probability that the feature captures the structure of the data set at hand. In particular, the significance is equal to a value proportional to the variance of a feature within a particular data set. The larger the variance, the higher the probability that the feature will capture the underlying structure. This approach is particularly useful when significance is used to select features differentiating clusters of samples and for the construction of self-organizing maps (SOMs) of clusters. A significance spectrum is obtained by plotting significance as a function of wavenumber. After developing the approach for feature significance, the significance framework was applied to the construction of SOMs for clustering infrared spectra of bacteria. The significance framework consistently chooses features that make it possible to construct maps with reduced feature sets that are at least as good as the maps constructed on full feature sets. In addition, significance reliably picks features that are consistent with biological interpretations of the spectra.

Keywords: Bacteria spectra; Feature selection; Infrared spectra; Self-organizing maps; Significance spectrum; Spectroscopy

Document Type: Research Article


Affiliations: 1: Department of Computer Science and Statistics, University of Rhode Island, Kingston, Rhode Island 02874 2: Department of Chemistry (C.W.B.), University of Rhode Island, Kingston, Rhode Island 02874

Publication date: January 1, 2012

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