Feature extraction using sparse component decomposition for face classification
In the recent years, the feature extraction as an intermediate step in the classification, has attracted the attention of researchers. In this paper, a new supervised feature extraction method is proposed using sparse component decomposition. The proposed algorithm has two steps.In the first step, t...
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University of Qom
2023-09-01
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| Series: | مدیریت مهندسی و رایانش نرم |
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| Online Access: | https://jemsc.qom.ac.ir/article_2345_96ab4eaf4bd34a5b409dadf13e8ffaac.pdf |
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| _version_ | 1832577524255686656 |
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| author | Hamid Reza Shahdoosti |
| author_facet | Hamid Reza Shahdoosti |
| author_sort | Hamid Reza Shahdoosti |
| collection | DOAJ |
| description | In the recent years, the feature extraction as an intermediate step in the classification, has attracted the attention of researchers. In this paper, a new supervised feature extraction method is proposed using sparse component decomposition. The proposed algorithm has two steps.In the first step, the common information of the data matrix is extracted in a low rank matrix. In he second step, a linear feature extractor method such as local preservation projection one is used to extract the final features. Then, the extracted features are fed to the support vector machine classifier. To evaluate the accuracy rate of the proposed method, three datasets are used. The results show that the proposed method outperforms compared with some state of the art methods. |
| format | Article |
| id | doaj-art-3aa19fff839445dd91cb85a6cd2ca729 |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2023-09-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-3aa19fff839445dd91cb85a6cd2ca7292025-01-30T20:18:53ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752023-09-0191647410.22091/JEMSC.2022.6975.11502345Feature extraction using sparse component decomposition for face classificationHamid Reza Shahdoosti0Assistance Prof. Electrical and Computer Engineering Department,, Hamedan University of tecnology. Hamedan, Iran. Email: h.doosti@hut.ac.irIn the recent years, the feature extraction as an intermediate step in the classification, has attracted the attention of researchers. In this paper, a new supervised feature extraction method is proposed using sparse component decomposition. The proposed algorithm has two steps.In the first step, the common information of the data matrix is extracted in a low rank matrix. In he second step, a linear feature extractor method such as local preservation projection one is used to extract the final features. Then, the extracted features are fed to the support vector machine classifier. To evaluate the accuracy rate of the proposed method, three datasets are used. The results show that the proposed method outperforms compared with some state of the art methods.https://jemsc.qom.ac.ir/article_2345_96ab4eaf4bd34a5b409dadf13e8ffaac.pdffeature extractionface classificationsparse decompositionsupport vector machine |
| spellingShingle | Hamid Reza Shahdoosti Feature extraction using sparse component decomposition for face classification مدیریت مهندسی و رایانش نرم feature extraction face classification sparse decomposition support vector machine |
| title | Feature extraction using sparse component decomposition for face classification |
| title_full | Feature extraction using sparse component decomposition for face classification |
| title_fullStr | Feature extraction using sparse component decomposition for face classification |
| title_full_unstemmed | Feature extraction using sparse component decomposition for face classification |
| title_short | Feature extraction using sparse component decomposition for face classification |
| title_sort | feature extraction using sparse component decomposition for face classification |
| topic | feature extraction face classification sparse decomposition support vector machine |
| url | https://jemsc.qom.ac.ir/article_2345_96ab4eaf4bd34a5b409dadf13e8ffaac.pdf |
| work_keys_str_mv | AT hamidrezashahdoosti featureextractionusingsparsecomponentdecompositionforfaceclassification |