Bayesian nonparametric classification for spectroscopy data

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.