Support vector machines with the correlation kernel for the classification of Raman spectra

Alexandros Kyriakides; Evdokia Kastanos; Katerina Hadjigeorgiou; Costas Pitris

doi:10.1117/12.873308

21 February 2011 Support vector machines with the correlation kernel for the classification of Raman spectra

Alexandros Kyriakides, Evdokia Kastanos, Katerina Hadjigeorgiou, Costas Pitris

Proceedings Volume 7890, Advanced Biomedical and Clinical Diagnostic Systems IX; 78901B (2011) https://doi.org/10.1117/12.873308
Event: SPIE BiOS, 2011, San Francisco, California, United States

Abstract

The range of applications of Raman-based classification has expanded significantly, including applications in bacterial identification. The first stage in the classification of Raman spectra is commonly some form of preprocessing. This pre-processing greatly affects the accuracy of the results and introduces user bias and over-fitting effects. In this paper, we propose the use of Support Vector Machines with a novel correlation kernel. Results, obtained from the analysis of Raman spectra of bacteria, illustrate that the correlation kernel is "self-normalizing" and produces superior classification performance with minimal pre-processing, even on highly-noisy data obtained using inexpensive equipment. In addition, the performance does not degrade when applied to distinct test sets, a key feature of a clinically viable diagnostic application of Raman Spectroscopy.

Citation Download Citation

Alexandros Kyriakides, Evdokia Kastanos, Katerina Hadjigeorgiou, and Costas Pitris "Support vector machines with the correlation kernel for the classification of Raman spectra", Proc. SPIE 7890, Advanced Biomedical and Clinical Diagnostic Systems IX, 78901B (21 February 2011); https://doi.org/10.1117/12.873308

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