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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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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. |
format | Online Article Text |
id | pubmed-3984787 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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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