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...
| 出版年: | مدیریت مهندسی و رایانش نرم |
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| 第一著者: | |
| フォーマット: | 論文 |
| 言語: | ペルシア語 |
| 出版事項: |
University of Qom
2023-09-01
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| 主題: | |
| オンライン･アクセス: | https://jemsc.qom.ac.ir/article_2345_96ab4eaf4bd34a5b409dadf13e8ffaac.pdf |
| 要約: | 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. |
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| ISSN: | 2538-6239 2538-2675 |