Raman spectra classification with support vector machines and a correlation kernel

Alexandros Kyriakides; Evdokia Kastanos; Katerina Hadjigeorgiou; Costas Pitris

doi:10.1117/12.889763

8 June 2011 Raman spectra classification with support vector machines and a correlation kernel

Alexandros Kyriakides, Evdokia Kastanos, Katerina Hadjigeorgiou, Costas Pitris

Author Affiliations +

Proceedings Volume 8087, Clinical and Biomedical Spectroscopy and Imaging II; 808706 (2011) https://doi.org/10.1117/12.889763
Event: European Conferences on Biomedical Optics, 2011, Munich, Germany

Abstract

Support Vector Machines have been used successfully for the classification of data in a wide range of applications. A key factor affecting the accuracy of the classification is the choice of kernel. In this paper we propose the use of Support Vector Machines with a correlation kernel. The correlation kernel is an appropriate choice when performing classification of Raman spectra because it reduces the need for pre-processing. Pre-processing can greatly affect the accuracy of the results because it introduces user bias and over-fitting effects. The correlation kernel is "self-normalizing" and produces superior classification performance with minimal pre-processing. Our results show that the performance on highly-noisy data, obtained using inexpensive equipment, is still high even when the classification is applied on a distinct hold-out set of test data. This is an important consideration when developing clinically viable diagnostic applications.

Citation Download Citation

Alexandros Kyriakides, Evdokia Kastanos, Katerina Hadjigeorgiou, and Costas Pitris "Raman spectra classification with support vector machines and a correlation kernel", Proc. SPIE 8087, Clinical and Biomedical Spectroscopy and Imaging II, 808706 (8 June 2011); https://doi.org/10.1117/12.889763

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