Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection
Nonnegative Matrix Factorization (NMF) is a significant big data analysis technique. However, standard NMF regularized by simple graph does not have discriminative function, and traditional graph models cannot accurately reflect the problem of multigeometry information between data. To solve the abo...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/7081674 |
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| author | Yong-Jing Hao Ying-Lian Gao Mi-Xiao Hou Ling-Yun Dai Jin-Xing Liu |
| author_facet | Yong-Jing Hao Ying-Lian Gao Mi-Xiao Hou Ling-Yun Dai Jin-Xing Liu |
| author_sort | Yong-Jing Hao |
| collection | DOAJ |
| description | Nonnegative Matrix Factorization (NMF) is a significant big data analysis technique. However, standard NMF regularized by simple graph does not have discriminative function, and traditional graph models cannot accurately reflect the problem of multigeometry information between data. To solve the above problem, this paper proposed a new method called Hypergraph Regularized Discriminative Nonnegative Matrix Factorization (HDNMF), which captures intrinsic geometry by constructing hypergraphs rather than simple graphs. The introduction of the hypergraph method allows high-order relationships between samples to be considered, and the introduction of label information enables the method to have discriminative effect. Both the hypergraph Laplace and the discriminative label information are utilized together to learn the projection matrix in the standard method. In addition, we offered a corresponding multiplication update solution for the optimization. Experiments indicate that the method proposed is more effective by comparing with the earlier methods. |
| format | Article |
| id | doaj-art-b7cd818a49504b1b8c367d0d9667e623 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b7cd818a49504b1b8c367d0d9667e6232025-02-03T05:58:15ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/70816747081674Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene SelectionYong-Jing Hao0Ying-Lian Gao1Mi-Xiao Hou2Ling-Yun Dai3Jin-Xing Liu4School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaLibrary of Qufu Normal University, Qufu Normal University, Rizhao 276826, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaNonnegative Matrix Factorization (NMF) is a significant big data analysis technique. However, standard NMF regularized by simple graph does not have discriminative function, and traditional graph models cannot accurately reflect the problem of multigeometry information between data. To solve the above problem, this paper proposed a new method called Hypergraph Regularized Discriminative Nonnegative Matrix Factorization (HDNMF), which captures intrinsic geometry by constructing hypergraphs rather than simple graphs. The introduction of the hypergraph method allows high-order relationships between samples to be considered, and the introduction of label information enables the method to have discriminative effect. Both the hypergraph Laplace and the discriminative label information are utilized together to learn the projection matrix in the standard method. In addition, we offered a corresponding multiplication update solution for the optimization. Experiments indicate that the method proposed is more effective by comparing with the earlier methods.http://dx.doi.org/10.1155/2019/7081674 |
| spellingShingle | Yong-Jing Hao Ying-Lian Gao Mi-Xiao Hou Ling-Yun Dai Jin-Xing Liu Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection Complexity |
| title | Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection |
| title_full | Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection |
| title_fullStr | Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection |
| title_full_unstemmed | Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection |
| title_short | Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection |
| title_sort | hypergraph regularized discriminative nonnegative matrix factorization on sample classification and co differentially expressed gene selection |
| url | http://dx.doi.org/10.1155/2019/7081674 |
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