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On improving robustness of LDA and SRDA by using tangent vectors

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On improving robustness of LDA and SRDA by using tangent vectors

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Villegas, M.; Paredes Palacios, R. (2013). On improving robustness of LDA and SRDA by using tangent vectors. Pattern Recognition Letters. 34(9):1094-1100. doi:10.1016/j.patrec.2013.03.001.

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Título: On improving robustness of LDA and SRDA by using tangent vectors
Autor:
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
In the area of pattern recognition, it is common for few training samples to be available with respect to the dimensionality of the representation space; this is known as the curse of dimensionality. This problem can be ...[+]
Palabras clave: Subspace learning , Dimensionality reduction , Tangent vectors , LDA , SRDA
Derechos de uso: Reserva de todos los derechos
Fuente:
Pattern Recognition Letters. (issn: 0167-8655 )
DOI: 10.1016/j.patrec.2013.03.001
Editorial:
Elsevier
Versión del editor: http://dx.doi.org/10.1016/j.patrec.2013.03.001
Código del Proyecto: info:eu-repo/grantAgreement/EC/FP7/600707
Descripción: NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, [Volume 34, Issue 9, 1 July 2013, Pages 1094–1100] DOI: 10.1016/j.patrec.2013.03.001
Patrocinador:
EU 7th Framework Programme grant tranScriptorium (Ref: 600707)
Spanish MEC under the STraDA research project (TIN2012–37475-C02–01)
Generalitat Valenciana under grant Prometeo/2009/014.
Tipo: Artículo

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