Por favor, use este identificador para citar o enlazar este ítem:

http://hdl.handle.net/10609/1325
Título: Shared feature extraction for nearest neighbor face recognition
Autor: Masip Rodó, David
Vitrià, Jordi
Fecha de publicación: 2008
Citación: Masip, D.; Vitrià, J. (2008). "Shared Feature Extraction for Nearest Neighbor Face Recognition". IEEE transactions on neural networks. n. 4, p. 586-595. ISSN: 1045-9227.
Resumen: In this paper, we propose a new supervised linear feature extraction technique for multiclass classification problems that is specially suited to the nearest neighbor classifier (NN). The problem of finding the optimal linear projection matrix is defined as a classification problem and the Adaboost algorithm is used to compute it in an iterative way. This strategy allows the introduction of a multitask learning (MTL) criterion in the method and results in a solution that makes no assumptions about the data distribution and that is specially appropriated to solve the small sample size problem. The performance of the method is illustrated by an application to the face recognition problem. The experiments show that the representation obtained following the multitask approach improves the classic feature extraction algorithms when using the NN classifier, especially when we have a few examples from each class
URI: http://hdl.handle.net/10609/1325
ISSN: 1045-9227
Aparece en las colecciones: Artículos

Comparte:
Exporta:
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
Masip_IEEETNN2008_Shared.pdf1,12 MBAdobe PDFVisualizar/Abrir

Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons Creative Commons