Sparse Graph Embedding Based on the Fuzzy Set for Image Classification
In recent years, many face image feature extraction and dimensional reduction algorithms have been proposed for linear and nonlinear data, such as local-based graph embedding algorithms or fuzzy set algorithms. However, the aforementioned algorithms are not very effective for face images because the...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6638985 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832566417369595904 |
|---|---|
| author | Minghua Wan Mengting Ge Tianming Zhan Zhangjing Yang Hao Zheng Guowei Yang |
| author_facet | Minghua Wan Mengting Ge Tianming Zhan Zhangjing Yang Hao Zheng Guowei Yang |
| author_sort | Minghua Wan |
| collection | DOAJ |
| description | In recent years, many face image feature extraction and dimensional reduction algorithms have been proposed for linear and nonlinear data, such as local-based graph embedding algorithms or fuzzy set algorithms. However, the aforementioned algorithms are not very effective for face images because they are always affected by overlaps (outliers) and sparsity points in the database. To solve the problems, a new and effective dimensional reduction method for face recognition is proposed—sparse graph embedding with the fuzzy set for image classification. The aim of this algorithm is to construct two new fuzzy Laplacian scattering matrices by using the local graph embedding and fuzzy k-nearest neighbor. Finally, the optimal discriminative sparse projection matrix is obtained by adding elastic network regression. Experimental results and analysis indicate that the proposed algorithm is more effective than other algorithms in the UCI wine dataset and ORL, Yale, and AR standard face databases. |
| format | Article |
| id | doaj-art-8728a5a642254b878e59b4d6250dc71f |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8728a5a642254b878e59b4d6250dc71f2025-02-03T01:04:13ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66389856638985Sparse Graph Embedding Based on the Fuzzy Set for Image ClassificationMinghua Wan0Mengting Ge1Tianming Zhan2Zhangjing Yang3Hao Zheng4Guowei Yang5School of Information Engineeering, Nanjing Audit University, Nanjing 211815, ChinaSchool of Information Engineeering, Nanjing Audit University, Nanjing 211815, ChinaSchool of Information Engineeering, Nanjing Audit University, Nanjing 211815, ChinaSchool of Information Engineeering, Nanjing Audit University, Nanjing 211815, ChinaKey Laboratory of Intelligent Information Processing, Nanjing Xiaozhuang University, Nanjing 211171, ChinaSchool of Information Engineeering, Nanjing Audit University, Nanjing 211815, ChinaIn recent years, many face image feature extraction and dimensional reduction algorithms have been proposed for linear and nonlinear data, such as local-based graph embedding algorithms or fuzzy set algorithms. However, the aforementioned algorithms are not very effective for face images because they are always affected by overlaps (outliers) and sparsity points in the database. To solve the problems, a new and effective dimensional reduction method for face recognition is proposed—sparse graph embedding with the fuzzy set for image classification. The aim of this algorithm is to construct two new fuzzy Laplacian scattering matrices by using the local graph embedding and fuzzy k-nearest neighbor. Finally, the optimal discriminative sparse projection matrix is obtained by adding elastic network regression. Experimental results and analysis indicate that the proposed algorithm is more effective than other algorithms in the UCI wine dataset and ORL, Yale, and AR standard face databases.http://dx.doi.org/10.1155/2021/6638985 |
| spellingShingle | Minghua Wan Mengting Ge Tianming Zhan Zhangjing Yang Hao Zheng Guowei Yang Sparse Graph Embedding Based on the Fuzzy Set for Image Classification Complexity |
| title | Sparse Graph Embedding Based on the Fuzzy Set for Image Classification |
| title_full | Sparse Graph Embedding Based on the Fuzzy Set for Image Classification |
| title_fullStr | Sparse Graph Embedding Based on the Fuzzy Set for Image Classification |
| title_full_unstemmed | Sparse Graph Embedding Based on the Fuzzy Set for Image Classification |
| title_short | Sparse Graph Embedding Based on the Fuzzy Set for Image Classification |
| title_sort | sparse graph embedding based on the fuzzy set for image classification |
| url | http://dx.doi.org/10.1155/2021/6638985 |
| work_keys_str_mv | AT minghuawan sparsegraphembeddingbasedonthefuzzysetforimageclassification AT mengtingge sparsegraphembeddingbasedonthefuzzysetforimageclassification AT tianmingzhan sparsegraphembeddingbasedonthefuzzysetforimageclassification AT zhangjingyang sparsegraphembeddingbasedonthefuzzysetforimageclassification AT haozheng sparsegraphembeddingbasedonthefuzzysetforimageclassification AT guoweiyang sparsegraphembeddingbasedonthefuzzysetforimageclassification |