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Título:
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Shared feature extraction for nearest neighbor face recognition
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Autor/a:
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Masip Rodo, David; Vitrià, Jordi
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Abstract:
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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 |
Materia(s):
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-Educational technology -Human face recognition (Computer science) -Tecnologia educativa -Reconeixement facial (Informàtica) -Tecnología educativa -Reconocimiento facial (Informática) |
Derechos:
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https://creativecommons.org/licenses/by-nc-nd/2.5/es/ |
Tipo de documento:
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Artículo |
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