A novel general kernel-based non-negative matrix factorisation approach for face recognition
Kernel-based non-negative matrix factorisation (KNMF) is a promising nonlinear approach for image data representation using non-negative features. However, most of the KNMF algorithms are developed via a specific kernel function and thus fail to adopt other kinds of kernels. Also, they have to learn...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2021.1988904 |
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| author | Wen-Sheng Chen Xiya Ge Binbin Pan |
| author_facet | Wen-Sheng Chen Xiya Ge Binbin Pan |
| author_sort | Wen-Sheng Chen |
| collection | DOAJ |
| description | Kernel-based non-negative matrix factorisation (KNMF) is a promising nonlinear approach for image data representation using non-negative features. However, most of the KNMF algorithms are developed via a specific kernel function and thus fail to adopt other kinds of kernels. Also, they have to learn pre-image inaccurately that may influence the reliability of the method. To address these problems of KNMF, this paper proposes a novel general kernel-based non-negative matrix factorisation (GKBNNMF) method. It not only avoids pre-image learning but also is suitable for any kernel functions as well. We assume that the mapped basis images fall within the cone spanned by the mapped training data, allowing us to use arbitrary kernel function in the algorithm. The symmetric NMF strategy is exploited on kernel matrix to establish our general kernel NMF model. The proposed algorithm is proven to be convergent. The facial image datasets are selected to evaluate the performance of our method. Compared with some state-of-the-art approaches, the experimental results demonstrate that our proposed is both effective and robust. |
| format | Article |
| id | doaj-art-8bdf69c36ada4a9caae229f5e3cae278 |
| institution | Kabale University |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-8bdf69c36ada4a9caae229f5e3cae2782025-08-20T03:37:38ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-0134178581010.1080/09540091.2021.19889041988904A novel general kernel-based non-negative matrix factorisation approach for face recognitionWen-Sheng Chen0Xiya Ge1Binbin Pan2Shenzhen UniversityShenzhen UniversityShenzhen UniversityKernel-based non-negative matrix factorisation (KNMF) is a promising nonlinear approach for image data representation using non-negative features. However, most of the KNMF algorithms are developed via a specific kernel function and thus fail to adopt other kinds of kernels. Also, they have to learn pre-image inaccurately that may influence the reliability of the method. To address these problems of KNMF, this paper proposes a novel general kernel-based non-negative matrix factorisation (GKBNNMF) method. It not only avoids pre-image learning but also is suitable for any kernel functions as well. We assume that the mapped basis images fall within the cone spanned by the mapped training data, allowing us to use arbitrary kernel function in the algorithm. The symmetric NMF strategy is exploited on kernel matrix to establish our general kernel NMF model. The proposed algorithm is proven to be convergent. The facial image datasets are selected to evaluate the performance of our method. Compared with some state-of-the-art approaches, the experimental results demonstrate that our proposed is both effective and robust.http://dx.doi.org/10.1080/09540091.2021.1988904non-negative matrix factorisationkernel functionface recognition |
| spellingShingle | Wen-Sheng Chen Xiya Ge Binbin Pan A novel general kernel-based non-negative matrix factorisation approach for face recognition Connection Science non-negative matrix factorisation kernel function face recognition |
| title | A novel general kernel-based non-negative matrix factorisation approach for face recognition |
| title_full | A novel general kernel-based non-negative matrix factorisation approach for face recognition |
| title_fullStr | A novel general kernel-based non-negative matrix factorisation approach for face recognition |
| title_full_unstemmed | A novel general kernel-based non-negative matrix factorisation approach for face recognition |
| title_short | A novel general kernel-based non-negative matrix factorisation approach for face recognition |
| title_sort | novel general kernel based non negative matrix factorisation approach for face recognition |
| topic | non-negative matrix factorisation kernel function face recognition |
| url | http://dx.doi.org/10.1080/09540091.2021.1988904 |
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