title: Automatic identification of novel bacteria using Raman spectroscopy and Gaussian processes.
abstract: Raman spectroscopy is successfully used for the reliable classification of complex biological samples. Much effort concentrates on the accurate prediction of known categories for highly relevant tasks in a wide area of applications such as cancer detection and bacteria recognition. However, the resulting recognition systems cannot always be directly used in practice since unseen samples might not belong to classes present in the training set. Our work aims to tackle this problem of novelty detection using a recently proposed approach based on Gaussian processes. By learning novelty scores for a large bacteria Raman dataset comprising 50 different strains, we analyze the behavior of this method on an independent dataset which includes known as well as unknown categories. Our experiment reveals that non-parametric methods such as Gaussian processes can be successfully applied to the task of finding unknown bacterial strains, leading to encouraging results motivating their further utilization in this area.
authors: Computer Vision Research Group, Department of Mathematics and Computer Science, Friedrich Schiller University of Jena, Germany. michael.Kemmler@uni-jena.de
authors: Michael
authors: M
authors: Kemmler
authors: Erik
authors: E
authors: Rodner
authors: Petra
authors: P
authors: Rösch
authors: Jürgen
authors: J
authors: Popp
authors: Joachim
authors: J
authors: Denzler
mesh_terms: D001419:Bacteria / Q000737:chemistry / Q000145:classification*; D015373:Bacterial Typing Techniques / Q000379:methods*; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D013059:Spectrum Analysis, Raman
keywords: Bacteria recognition; Gaussian processes; Novelty detection; One-class classification; Raman spectroscopy