Classification of Microorganisms via Raman Spectroscopy Using Gaussian Processes
Automatic categorization of microorganisms is a complex task which requires advanced techniques to achieve accurate performance. In this paper, we aim at identifying microorganisms based on Raman spectroscopy. Empirical studies over the last years show that powerful machine learning methods such as Support Vector Machines (SVMs) are suitable for this task. Our work focuses on the Gaussian process (GP) classifier which is new to this field, provides fully probabilistic outputs and allows for efficient hyperparameter optimization. We also investigate the incorporation of prior knowledge regarding possible signal variations where known concepts from invariant kernel theory are transferred to the GP framework. In order to validate the suitability of the GP classifier, a comparison with state-of-the-art learners is conducted on a large-scale Raman spectra dataset, showing that the GP classifier significantly outperforms all other tested classifiers including SVM. Our results further show that incorporating prior knowledge leads to a significant performance gain when small amounts of training data are used.
KeywordsRaman Spectrum Raman Spectroscopy Covariance Function Gaussian Process Average Recognition Rate
Unable to display preview. Download preview PDF.
- 2.Rösch, P., Harz, M., Peschke, K.D., Ronneberger, O., Burkhardt, H., Motzkus, H.W., Lankers, M., Hofer, S., Thiele, H., Popp, J.: Chemotaxonomic identification of single bacteria by micro-raman spectroscopy: Application to clean-room-relevant biological contaminations. Applied and Environmental Microbiology 71, 1626–1637 (2005)CrossRefGoogle Scholar
- 6.Peschke, K.D., Haasdonk, B., Ronneberger, O., Burkhardt, H., Rösch, P., Harz, M., Popp, J.: Using transformation knowledge for the classification of raman spectra of biological samples. In: Proceedings of the 24th IASTED international conference on Biomedical engineering, Anaheim, CA, USA, pp. 288–293. ACTA Press (2006)Google Scholar
- 8.Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. The MIT Press, Cambridge (2005)Google Scholar
- 9.Haasdonk, B.: Transformation Knowledge in Pattern Analysis with Kernel Methods. PhD thesis, Computer Science Department, University of Freiburg (2005)Google Scholar
- 11.Casagrande, N.: Multiboost: An open source multi-class adaboost learner (2005), http://multiboost.sourceforge.net/
- 12.Joachims, T.: Making large-Scale SVM Learning Practical. MIT-Press, Cambridge (1999)Google Scholar
- 13.Chen, Y., Gupta, M.R., Recht, B.: Learning kernels from indefinite similarities. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 145–152. ACM, New York (2009)Google Scholar