Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes
Semisupervised learning is an idea that addresses how to use a large number of unlabeled samples and a limited number of labeled samples to learn decision knowledge together. In this paper, we propose a multitask multiview semisupervised learning model based on partial differential equation random f...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Mathematical Physics |
| Online Access: | http://dx.doi.org/10.1155/2021/4784411 |
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| author | Wei Song Guang Hu Liuqing OuYang Zhenjie Zhu |
| author_facet | Wei Song Guang Hu Liuqing OuYang Zhenjie Zhu |
| author_sort | Wei Song |
| collection | DOAJ |
| description | Semisupervised learning is an idea that addresses how to use a large number of unlabeled samples and a limited number of labeled samples to learn decision knowledge together. In this paper, we propose a multitask multiview semisupervised learning model based on partial differential equation random field and Hilbert independent standard probability image genus attribute model, i.e., shared semantics. In the framework of the image-like genus attribute model, data from different data sources are generated by their shared hidden space representation. Different from the traditional model, this paper uses the Hilbert independence criterion to inscribe the shared relationship of hidden expressions. Meanwhile, to exploit the correlations between labels in the label space as well, this paper uses the partial differential equation random field to inscribe the correlations between different kinds of labels in the label space and the correlations between hidden features and labels. Using the variational expectation-maximization algorithm, the whole generative process model can be inferred. To verify the effectiveness of the model, two artificial datasets and three real datasets are tested in this paper, and the experimental results verify the effectiveness of the algorithm in the paper. On the one hand, it not only improves the classification accuracy of the multiclassification problem and the multilabel problem; it also outputs the association structure between different kinds of labels and between hidden features and labels. |
| format | Article |
| id | doaj-art-ecc85b072c0747bc8329f17a3dc3104d |
| institution | Kabale University |
| issn | 1687-9120 1687-9139 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Mathematical Physics |
| spelling | doaj-art-ecc85b072c0747bc8329f17a3dc3104d2025-02-03T05:47:37ZengWileyAdvances in Mathematical Physics1687-91201687-91392021-01-01202110.1155/2021/47844114784411Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class AttributesWei Song0Guang Hu1Liuqing OuYang2Zhenjie Zhu3School of Sports Engineering and Information Technology, Wuhan Sports University, Wuhan Hubei 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan Hubei 430072, ChinaSchool of Physical Education, Wuhan Sports University, Wuhan Hubei 430079, ChinaSchool of Physical Education, Wuhan Sports University, Wuhan Hubei 430079, ChinaSemisupervised learning is an idea that addresses how to use a large number of unlabeled samples and a limited number of labeled samples to learn decision knowledge together. In this paper, we propose a multitask multiview semisupervised learning model based on partial differential equation random field and Hilbert independent standard probability image genus attribute model, i.e., shared semantics. In the framework of the image-like genus attribute model, data from different data sources are generated by their shared hidden space representation. Different from the traditional model, this paper uses the Hilbert independence criterion to inscribe the shared relationship of hidden expressions. Meanwhile, to exploit the correlations between labels in the label space as well, this paper uses the partial differential equation random field to inscribe the correlations between different kinds of labels in the label space and the correlations between hidden features and labels. Using the variational expectation-maximization algorithm, the whole generative process model can be inferred. To verify the effectiveness of the model, two artificial datasets and three real datasets are tested in this paper, and the experimental results verify the effectiveness of the algorithm in the paper. On the one hand, it not only improves the classification accuracy of the multiclassification problem and the multilabel problem; it also outputs the association structure between different kinds of labels and between hidden features and labels.http://dx.doi.org/10.1155/2021/4784411 |
| spellingShingle | Wei Song Guang Hu Liuqing OuYang Zhenjie Zhu Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes Advances in Mathematical Physics |
| title | Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes |
| title_full | Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes |
| title_fullStr | Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes |
| title_full_unstemmed | Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes |
| title_short | Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes |
| title_sort | semisupervised association learning based on partial differential equations for sparse representation of image class attributes |
| url | http://dx.doi.org/10.1155/2021/4784411 |
| work_keys_str_mv | AT weisong semisupervisedassociationlearningbasedonpartialdifferentialequationsforsparserepresentationofimageclassattributes AT guanghu semisupervisedassociationlearningbasedonpartialdifferentialequationsforsparserepresentationofimageclassattributes AT liuqingouyang semisupervisedassociationlearningbasedonpartialdifferentialequationsforsparserepresentationofimageclassattributes AT zhenjiezhu semisupervisedassociationlearningbasedonpartialdifferentialequationsforsparserepresentationofimageclassattributes |