Article

Cognitive Computation

, Volume 2, Issue 3, pp 160-164

First online:

Reducing Features Using Discriminative Common Vectors

  • Carlos M. TraviesoAffiliated withSignals and Communications Department, IDeTIC, University of Las Palmas de Gran Canaria Email author 
  • , Marcos del PozoAffiliated withSignals and Communications Department, IDeTIC, University of Las Palmas de Gran Canaria
  • , Miguel A. FerrerAffiliated withSignals and Communications Department, IDeTIC, University of Las Palmas de Gran Canaria
  • , Jesús B. AlonsoAffiliated withSignals and Communications Department, IDeTIC, University of Las Palmas de Gran Canaria

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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