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Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition

In order to overcome the limitation of traditional nonnegative factorization algorithms, the paper presents a generalized discriminant orthogonal non-negative tensor factorization algorithm. At first, the algorithm takes the orthogonal constraint into account to ensure the nonnegativity of the low-d...

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Detalles Bibliográficos
Autores principales: XiuJun, Zhang, Chang, Liu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3984787/
https://www.ncbi.nlm.nih.gov/pubmed/24982970
http://dx.doi.org/10.1155/2014/608158
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author XiuJun, Zhang
Chang, Liu
author_facet XiuJun, Zhang
Chang, Liu
author_sort XiuJun, Zhang
collection PubMed
description In order to overcome the limitation of traditional nonnegative factorization algorithms, the paper presents a generalized discriminant orthogonal non-negative tensor factorization algorithm. At first, the algorithm takes the orthogonal constraint into account to ensure the nonnegativity of the low-dimensional features. Furthermore, the discriminant constraint is imposed on low-dimensional weights to strengthen the discriminant capability of the low-dimensional features. The experiments on facial expression recognition have demonstrated that the algorithm is superior to other non-negative factorization algorithms.
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spelling pubmed-39847872014-06-30 Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition XiuJun, Zhang Chang, Liu ScientificWorldJournal Research Article In order to overcome the limitation of traditional nonnegative factorization algorithms, the paper presents a generalized discriminant orthogonal non-negative tensor factorization algorithm. At first, the algorithm takes the orthogonal constraint into account to ensure the nonnegativity of the low-dimensional features. Furthermore, the discriminant constraint is imposed on low-dimensional weights to strengthen the discriminant capability of the low-dimensional features. The experiments on facial expression recognition have demonstrated that the algorithm is superior to other non-negative factorization algorithms. Hindawi Publishing Corporation 2014 2014-03-26 /pmc/articles/PMC3984787/ /pubmed/24982970 http://dx.doi.org/10.1155/2014/608158 Text en Copyright © 2014 Z. XiuJun and L. Chang. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
XiuJun, Zhang
Chang, Liu
Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition
title Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition
title_full Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition
title_fullStr Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition
title_full_unstemmed Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition
title_short Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition
title_sort generalized discriminant orthogonal nonnegative tensor factorization for facial expression recognition
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3984787/
https://www.ncbi.nlm.nih.gov/pubmed/24982970
http://dx.doi.org/10.1155/2014/608158
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