Computing feature matrices using PCA-SVD hybrid method on small-scale systems
The task of performing feature extraction from input matrices is a well-known problem in biometric recognition. This paper aims to develop an effective method for reduction and decomposition on large matrices with low required computational resources and fast processing times. Our contribution is to...
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| Format: | Article |
| Language: | English |
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The University of Danang
2024-12-01
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| Series: | Tạp chí Khoa học và Công nghệ |
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| Online Access: | https://jst-ud.vn/jst-ud/article/view/9205 |
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| author | Le Tien Hung Vu Minh Trong Phan Viet Thanh Nguyen Le Van |
| author_facet | Le Tien Hung Vu Minh Trong Phan Viet Thanh Nguyen Le Van |
| author_sort | Le Tien Hung |
| collection | DOAJ |
| description | The task of performing feature extraction from input matrices is a well-known problem in biometric recognition. This paper aims to develop an effective method for reduction and decomposition on large matrices with low required computational resources and fast processing times. Our contribution is to design a PCA-SVD hybrid method that divides the feature extraction into two phases: PCA-based size reduction and SVD-based decomposition. In our method, PCA is first applied to a large matrix to extract its important components. The size of the reduced matrix is defined based on the characteristics of the original matrix and the computational capacity of the hardware system, which allows SVD to be applied later. As a result, our method can effectively handle large matrices, leading to significant performance improvements for biometric recognition applications on small computers. |
| format | Article |
| id | doaj-art-d9c498d99e994fb4b1127e78b72f84f7 |
| institution | DOAJ |
| issn | 1859-1531 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | The University of Danang |
| record_format | Article |
| series | Tạp chí Khoa học và Công nghệ |
| spelling | doaj-art-d9c498d99e994fb4b1127e78b72f84f72025-08-20T03:11:22ZengThe University of DanangTạp chí Khoa học và Công nghệ1859-15312024-12-01788210.31130/ud-jst.2024.218E9199Computing feature matrices using PCA-SVD hybrid method on small-scale systemsLe Tien Hung0Vu Minh Trong1Phan Viet Thanh2Nguyen Le Van3Military Technical Academy, VietnamMilitary Technical Academy, VietnamMilitary Technical Academy, VietnamMilitary Technical Academy, VietnamThe task of performing feature extraction from input matrices is a well-known problem in biometric recognition. This paper aims to develop an effective method for reduction and decomposition on large matrices with low required computational resources and fast processing times. Our contribution is to design a PCA-SVD hybrid method that divides the feature extraction into two phases: PCA-based size reduction and SVD-based decomposition. In our method, PCA is first applied to a large matrix to extract its important components. The size of the reduced matrix is defined based on the characteristics of the original matrix and the computational capacity of the hardware system, which allows SVD to be applied later. As a result, our method can effectively handle large matrices, leading to significant performance improvements for biometric recognition applications on small computers.https://jst-ud.vn/jst-ud/article/view/9205feature extractionbiometric recognitionpcasvdeigenvalueraspberry pi |
| spellingShingle | Le Tien Hung Vu Minh Trong Phan Viet Thanh Nguyen Le Van Computing feature matrices using PCA-SVD hybrid method on small-scale systems Tạp chí Khoa học và Công nghệ feature extraction biometric recognition pca svd eigenvalue raspberry pi |
| title | Computing feature matrices using PCA-SVD hybrid method on small-scale systems |
| title_full | Computing feature matrices using PCA-SVD hybrid method on small-scale systems |
| title_fullStr | Computing feature matrices using PCA-SVD hybrid method on small-scale systems |
| title_full_unstemmed | Computing feature matrices using PCA-SVD hybrid method on small-scale systems |
| title_short | Computing feature matrices using PCA-SVD hybrid method on small-scale systems |
| title_sort | computing feature matrices using pca svd hybrid method on small scale systems |
| topic | feature extraction biometric recognition pca svd eigenvalue raspberry pi |
| url | https://jst-ud.vn/jst-ud/article/view/9205 |
| work_keys_str_mv | AT letienhung computingfeaturematricesusingpcasvdhybridmethodonsmallscalesystems AT vuminhtrong computingfeaturematricesusingpcasvdhybridmethodonsmallscalesystems AT phanvietthanh computingfeaturematricesusingpcasvdhybridmethodonsmallscalesystems AT nguyenlevan computingfeaturematricesusingpcasvdhybridmethodonsmallscalesystems |