Abstract
A feature reduction system based on Discriminative Common Vector is presented and evaluated in this paper. The validation of this system was made with three databases, first one is DNA markers and the other two are The ORL Database of Face and The Yale Face Database. Moreover, a supervised classification system has been implemented with three different classifiers, achieving the best success rates with Support Vector Machines using Radial Basis Function kernel and a one-versus-all multiclass approach. The study shows clearly how our approach reduces the number of features and load times, keeping or improving the level of discrimination.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Zhi L, Shuixiu W, Xiaoqing W, Hao Y, Mingwen W, Jihua Y. Dimensional reduction based on independent component analysis for content based image retrieval. In: International joint conference on artificial intelligence; 2009. pp. 741–745.
Shiqing Z, Zhijin Z. Dimensionality reduction-based phoneme recognition. In: 9th international conference on signal processing; 2008. pp. 667–670.
Duda RO, Hart PE, Stork DG. Pattern classification. 2nd ed. New York: Wiley Interscience; 2000.
Cevikalp H, Neamtu M, Barkana A. The kernel common vector method: a novel nonlinear subspace classifier for pattern recognition. IEEE Trans Syst Man Cybern Part B. 2007;37(4):937–51.
Cevikalp H, Neamtu M, Wilkes M. Discriminative Common Vector method with kernels. IEEE Trans Neural Netw. 2006;17(6):1832–8.
Cristianini N, Shawe-Taylor J. An introduction to support vector machines. Cambridge: Cambridge University Press; 2000.
Joachims T. SVM_light support vector machine. Department of Computer Science, Cornell University, Version: 6.02. http://svmlight.joachims.org/ (2009). Last visit: 30 Mar 2009.
Mora-Urpí J, Arroyo C. Sobre origen y diversidad en pejibaye. Serie Técnica Pejibaye (Guilielma). Technical report, Ed. University of Costa Rica; 1996. Vol. 5, No 1, pp. 18–25.
Ferrer M, Eguiarte LE, Montana C. Genetic structure and outcrossing rates in Flourensia cernua (Asteraceae) growing at different densities in the South-western Chihuahuan Desert. Ann Bot. 2004;94:419–26.
http://www.uk.research.att.com/facedatabase.html (2009). Last visit: 30 Mar 2009.
http://cvc.yale.edu (2009). Last visit: 30 Mar 2009.
Mitchell TM. Machine learning. New York: McGraw-Hill; 1997.
Bishop CM. Neural networks for pattern recognition. Oxford: Oxford University Press; 1996.
Travieso CM, Botella P, Alonso JB, Ferrer MA. Discriminative Common Vector for face identification. In: 43rd IEEE international carnahan conference on security technology; 2009. pp. 134–138.
Briceño JC, Travieso CM, Ferrer MA, Alonso JB, Vargas F. Off-line signature recognition based on contour parameterization. In: 12th international conference on computer aided system theory; 2009. pp. 118–120.
Gonzalez RC, Wood RE. Digital image processing. Reading: Addison-Wesley; 2002.
Hyvärinen A, Karhunen J, Oja E. Independent component analysis. New York: Wiley; 2001.
Acknowledgments
This work was supported by the program “Programa José Castillejo 2008” from the Spanish Government under Grant JC2008-00398.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Travieso, C.M., del Pozo, M., Ferrer, M.A. et al. Reducing Features Using Discriminative Common Vectors. Cogn Comput 2, 160–164 (2010). https://doi.org/10.1007/s12559-010-9059-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12559-010-9059-y