Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction
High-dimensional signals, such as image signals and audio signals, usually have a sparse or low-dimensional manifold structure, which can be projected into a low-dimensional subspace to improve the efficiency and effectiveness of data processing. In this paper, we propose a linear dimensionality red...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/17/4778 |
| _version_ | 1827707798347055104 |
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| author | Haoshuang Hu Da-Zheng Feng |
| author_facet | Haoshuang Hu Da-Zheng Feng |
| author_sort | Haoshuang Hu |
| collection | DOAJ |
| description | High-dimensional signals, such as image signals and audio signals, usually have a sparse or low-dimensional manifold structure, which can be projected into a low-dimensional subspace to improve the efficiency and effectiveness of data processing. In this paper, we propose a linear dimensionality reduction method—minimum eigenvector collaborative representation discriminant projection—to address high-dimensional feature extraction problems. On the one hand, unlike the existing collaborative representation method, we use the eigenvector corresponding to the smallest non-zero eigenvalue of the sample covariance matrix to reduce the error of collaborative representation. On the other hand, we maintain the collaborative representation relationship of samples in the projection subspace to enhance the discriminability of the extracted features. Also, the between-class scatter of the reconstructed samples is used to improve the robustness of the projection space. The experimental results on the COIL-20 image object database, ORL, and FERET face databases, as well as Isolet database demonstrate the effectiveness of the proposed method, especially in low dimensions and small training sample size. |
| first_indexed | 2024-03-10T16:54:44Z |
| format | Article |
| id | doaj.art-35c8aa00faa449e7b300e4c9e69353cf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:54:44Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-35c8aa00faa449e7b300e4c9e69353cf2023-11-20T11:11:26ZengMDPI AGSensors1424-82202020-08-012017477810.3390/s20174778Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature ExtractionHaoshuang Hu0Da-Zheng Feng1National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaHigh-dimensional signals, such as image signals and audio signals, usually have a sparse or low-dimensional manifold structure, which can be projected into a low-dimensional subspace to improve the efficiency and effectiveness of data processing. In this paper, we propose a linear dimensionality reduction method—minimum eigenvector collaborative representation discriminant projection—to address high-dimensional feature extraction problems. On the one hand, unlike the existing collaborative representation method, we use the eigenvector corresponding to the smallest non-zero eigenvalue of the sample covariance matrix to reduce the error of collaborative representation. On the other hand, we maintain the collaborative representation relationship of samples in the projection subspace to enhance the discriminability of the extracted features. Also, the between-class scatter of the reconstructed samples is used to improve the robustness of the projection space. The experimental results on the COIL-20 image object database, ORL, and FERET face databases, as well as Isolet database demonstrate the effectiveness of the proposed method, especially in low dimensions and small training sample size.https://www.mdpi.com/1424-8220/20/17/4778collaborative representationdiscriminant projectionfeature extractionlinear dimensionality reductionsubspace projection |
| spellingShingle | Haoshuang Hu Da-Zheng Feng Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction Sensors collaborative representation discriminant projection feature extraction linear dimensionality reduction subspace projection |
| title | Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction |
| title_full | Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction |
| title_fullStr | Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction |
| title_full_unstemmed | Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction |
| title_short | Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction |
| title_sort | minimum eigenvector collaborative representation discriminant projection for feature extraction |
| topic | collaborative representation discriminant projection feature extraction linear dimensionality reduction subspace projection |
| url | https://www.mdpi.com/1424-8220/20/17/4778 |
| work_keys_str_mv | AT haoshuanghu minimumeigenvectorcollaborativerepresentationdiscriminantprojectionforfeatureextraction AT dazhengfeng minimumeigenvectorcollaborativerepresentationdiscriminantprojectionforfeatureextraction |