Finding discriminative features for Raman spectroscopy

To identify microorganisms is of utmost importance in various applications such as medical science and pharmaceutical industry. The technique of Raman spectroscopy is particularly useful in this scenario, since it extracts a high-dimensional molecular fingerprint from samples at hand. Instead of using the complete spectrum, it is often sensible to concentrate on a small number of discriminative dimensions. Apart from providing important molecular insights, this can be beneficial in terms of speed and accuracy. This work studies several state-of-the-art machine learning techniques suitable for feature ranking, many of which have not been used before in the context of Raman spectra classification. Experiments on three different bacteria classification problems show that boosting-based methods and zero-norm support vector machines are especially suited for this challenging task.