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Comparative diagnostic accuracy of linear and nonlinear feature extraction methods in a neuro-oncology problem

Author
Cruz, R.; Bautista-Villavicencio, D.; Vellido, A.
Type of activity
Presentation of work at congresses
Name of edition
3rd Mexican Conference on Pattern Recognition
Date of publication
2011
Presentation's date
2011
Book of congress proceedings
Pattern recognition: third Mexican conference, MCPR 2011, Cancun, Mexico, June 29-July 2, 2011: proceedings
First page
34
Last page
41
Publisher
Springer
DOI
https://doi.org/10.1007/978-3-642-21587-2_4 Open in new window
Project funding
AIDTUMOUR: HERRAMIENTAS BASADAS EN METODOS DE INTELIGENCIA ARTIFICIAL PARA EL APOYO A LA DECISION EN
Repository
http://hdl.handle.net/2117/18670 Open in new window
URL
http://link.springer.com/chapter/10.1007%2F978-3-642-21587-2_4 Open in new window
Abstract
The diagnostic classification of human brain tumours on the basis of magnetic resonance spectra is a non-trivial problem in which dimensionality reduction is almost mandatory. This may take the form of feature selection or feature extraction. In feature extraction using manifold learning models, multivariate data are described through a low-dimensional manifold embedded in data space. Similarities between points along this manifold are best expressed as geodesic distances or their approximations...
Citation
Cruz, R.; Bautista-Villavicencio, D.; Vellido, A. Comparative diagnostic accuracy of linear and nonlinear feature extraction methods in a neuro-oncology problem. A: Mexican Conference on Pattern Recognition. "Pattern recognition: third Mexican conference, MCPR 2011, Cancun, Mexico, June 29-July 2, 2011: proceedings". Cancún: Springer, 2011, p. 34-41.
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

Participants

  • Cruz Barbosa, Raul  (author and speaker )
  • Bautista Villavicencio, David  (author and speaker )
  • Vellido Alcacena, Alfredo  (author and speaker )