Reducing Features Using Discriminative Common Vectors
First Online: 03 August 2010 Received: 30 December 2009 Accepted: 25 July 2010 DOI:
Cite this article as: Travieso, C.M., del Pozo, M., Ferrer, M.A. et al. Cogn Comput (2010) 2: 160. doi:10.1007/s12559-010-9059-y 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.
Keywords Reduction features Discriminative Common Vector Machine learning Pattern Recognition References
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