Weighted LS-SVM for function estimation applied to artifact removal in bio-signal processing

Weighted LS-SVM is normally used for function estimation from highly corrupted data in order to decrease the impact of outliers. However, this method is limited in size and big time series should be segmented in smaller groups. Therefore, border discontinuities represent a problem in the final estim...

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Autores:
Tipo de recurso:
Fecha de publicación:
2010
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/28431
Acceso en línea:
https//doi.org 10.1109/IEMBS.2010.5627628
https://repository.urosario.edu.co/handle/10336/28431
Palabra clave:
Joints
Support vector machines
Estimation
Kernel
Biomedical measurements
Training
Robustness
Rights
License
Restringido (Acceso a grupos específicos)
Description
Summary:Weighted LS-SVM is normally used for function estimation from highly corrupted data in order to decrease the impact of outliers. However, this method is limited in size and big time series should be segmented in smaller groups. Therefore, border discontinuities represent a problem in the final estimated function. Several methods such as committee networks or multilayer networks of LS-SVMs are used to address this problem, but these methods require extra training and hence the computational cost is increased. In this paper a technique that includes an extra weight vector in the formulation of the cost function for the LS-SVM problem is proposed as an alternative solution. The method is then applied to the removal of some artifacts in biomedical signals.