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Proceedings Paper

Raman spectra classification with support vector machines and a correlation kernel
Author(s): Alexandros Kyriakides; Evdokia Kastanos; Katerina Hadjigeorgiou; Costas Pitris
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Paper 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.

Paper Details

Date Published: 8 June 2011
PDF: 7 pages
Proc. SPIE 8087, Clinical and Biomedical Spectroscopy and Imaging II, 808706 (8 June 2011); doi: 10.1117/12.889763
Show Author Affiliations
Alexandros Kyriakides, Univ. of Cyprus (Cyprus)
Evdokia Kastanos, Univ. of Nicosia (Cyprus)
Katerina Hadjigeorgiou, Univ. of Cyprus (Cyprus)
Costas Pitris, Univ. of Cyprus (Cyprus)


Published in SPIE Proceedings Vol. 8087:
Clinical and Biomedical Spectroscopy and Imaging II
Nirmala Ramanujam; Jürgen Popp, Editor(s)

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