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Bayesian nonparametric classification for spectroscopy data

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  • Gutiérrez, Luis
  • Gutiérrez-Peña, Eduardo
  • Mena, Ramsés H.

Abstract

High-dimensional spectroscopy data are increasingly common in many fields of science. Building classification models in this context is challenging, due not only to high dimensionality but also to high autocorrelations. A two-stage classification strategy is proposed. First, in a data pre-processing step, the dimensionality of the data is reduced using one of two distinct methods. The output of either of these methods is then used to feed a classification procedure that uses a multivariate density estimate from a Bayesian nonparametric mixture model for discrimination purposes. The model employed is based on a random probability measure with decreasing weights. This nonparametric prior is chosen so as to ease the identifiability and label switching problems inherent to these models. This simple and flexible classification strategy is applied to the well-known ‘meat’ data set. The results are similar or better than previously reported in the literature for the same data.

Suggested Citation

  • Gutiérrez, Luis & Gutiérrez-Peña, Eduardo & Mena, Ramsés H., 2014. "Bayesian nonparametric classification for spectroscopy data," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 56-68.
    • Handle: RePEc:eee:csdana:v:78:y:2014:i:c:p:56-68
    DOI: 10.1016/j.csda.2014.04.010
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    1. Crainiceanu, Ciprian M. & Ruppert, David & Wand, Matthew P., 2005. "Bayesian Analysis for Penalized Spline Regression Using WinBUGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i14).
    2. Hoefsloot Huub C. J. & Smit Suzanne & Smilde Age K., 2008. "A Classification Model for the Leiden Proteomics Competition," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-11, February.
    3. Nema Dean & Thomas Brendan Murphy & Gerard Downey, 2006. "Using unlabelled data to update classification rules with applications in food authenticity studies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(1), pages 1-14, January.
    4. Lee, Jong Soo & Cox, Dennis D., 2010. "Robust smoothing: Smoothing parameter selection and applications to fluorescence spectroscopy," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3131-3143, December.
    5. Rolando De la Cruz‐Mesía & Fernando A. Quintana & Peter Müller, 2007. "Semiparametric Bayesian classification with longitudinal markers," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(2), pages 119-137, March.
    6. Fearn Tom, 2008. "Principal Component Discriminant Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-6, February.
    7. Heidema A. Geert & Nagelkerke Nico, 2008. "Developing a Discrimination Rule between Breast Cancer Patients and Controls Using Proteomics Mass Spectrometric Data: A Three-Step Approach," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-11, February.
    8. Raftery, Adrian E. & Dean, Nema, 2006. "Variable Selection for Model-Based Clustering," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 168-178, March.
    9. Datta Somnath, 2008. "Classification of Breast Cancer versus Normal Samples from Mass Spectrometry Profiles Using Linear Discriminant Analysis of Important Features Selected by Random Forest," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-14, February.
    10. Yao, Weixin & Lindsay, Bruce G., 2009. "Bayesian Mixture Labeling by Highest Posterior Density," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 758-767.
    11. Luis Gutierrez & Fernando Quintana & Dietrich von Baer & Claudia Mardones, 2011. "Multivariate Bayesian discrimination for varietal authentication of Chilean red wine," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2099-2109.
    12. Chakraborty, Sounak, 2012. "Bayesian multiple response kernel regression model for high dimensional data and its practical applications in near infrared spectroscopy," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2742-2755.
    13. Anjishnu Banerjee & David B. Dunson & Surya T. Tokdar, 2013. "Efficient Gaussian process regression for large datasets," Biometrika, Biometrika Trust, vol. 100(1), pages 75-89.
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    1. De Blasi, Pierpaolo & Martínez, Asael Fabian & Mena, Ramsés H. & Prünster, Igor, 2020. "On the inferential implications of decreasing weight structures in mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
    2. Bao, Junshu & Hanson, Timothy E., 2016. "A mean-constrained finite mixture of normals model," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 93-99.
    3. Pierpaolo De Blasi & Ramsés H. Mena & Igor Prünster, 2022. "Asymptotic behavior of the number of distinct values in a sample from the geometric stick-breaking process," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(1), pages 143-165, February.

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