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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 Santamaría, 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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/40332

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Title: On improving robustness of LDA and SRDA by using tangent vectors
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Abstract:
[EN] 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 ...[+]
Subjects: Subspace learning , Dimensionality reduction , Tangent vectors , LDA , SRDA
Copyrigths: Reserva de todos los derechos
Source:
Pattern Recognition Letters. (issn: 0167-8655 )
DOI: 10.1016/j.patrec.2013.03.001
Publisher:
Elsevier
Publisher version: http://dx.doi.org/10.1016/j.patrec.2013.03.001
Project ID: info:eu-repo/grantAgreement/EC/FP7/600707
Description: 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
Thanks:
Work partially supported through the EU 7th Framework Programme grant tranScriptorium (Ref: 600707), by the Spanish MEC under the STraDA research project (TIN2012-37475-C02-01) and by the Generalitat Valenciana under grant ...[+]
Type: Artículo

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