Enhanced Multilinear PCA for Efficient Image Analysis and Dimensionality Reduction: Unlocking the Potential of Complex Image Data
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| Publicado no: | Mathematics vol. 13, no. 3 (2025), p. 531 |
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| Autor principal: | |
| Outros Autores: | , , |
| Publicado em: |
MDPI AG
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| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2227-7390 | ||
| 024 | 7 | |a 10.3390/math13030531 |2 doi | |
| 035 | |a 3165831060 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Sun, Tianyu | |
| 245 | 1 | |a Enhanced Multilinear PCA for Efficient Image Analysis and Dimensionality Reduction: Unlocking the Potential of Complex Image Data | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper presents an Enhanced Multilinear Principal Component Analysis (EMPCA) algorithm, an improved variant of the traditional Multilinear Principal Component Analysis (MPCA) tailored for efficient dimensionality reduction in high-dimensional data, particularly in image analysis tasks. EMPCA integrates random singular value decomposition to reduce computational complexity while maintaining data integrity. Additionally, it innovatively combines the dimensionality reduction method with the Mask R-CNN algorithm, enhancing the accuracy of image segmentation. Leveraging tensors, EMPCA achieves dimensionality reduction that specifically benefits image classification, face recognition, and image segmentation. The experimental results demonstrate a 17.7% reduction in computation time compared to conventional methods, without compromising accuracy. In image classification and face recognition experiments, EMPCA significantly enhances classifier efficiency, achieving comparable or superior accuracy to algorithms such as Support Vector Machines (SVMs). Additionally, EMPCA preprocessing exploits latent information within tensor structures, leading to improved segmentation performance. The proposed EMPCA algorithm holds promise for reducing image analysis runtimes and advancing rapid image processing techniques. | |
| 653 | |a Accuracy | ||
| 653 | |a Data analysis | ||
| 653 | |a Face recognition | ||
| 653 | |a Image analysis | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Singular value decomposition | ||
| 653 | |a Datasets | ||
| 653 | |a Support vector machines | ||
| 653 | |a Image segmentation | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Task complexity | ||
| 653 | |a Tensors | ||
| 653 | |a Decomposition | ||
| 653 | |a Image classification | ||
| 653 | |a Traffic flow | ||
| 653 | |a Algorithms | ||
| 653 | |a Methods | ||
| 653 | |a Eigenvalues | ||
| 653 | |a Image processing | ||
| 653 | |a Dimensional analysis | ||
| 700 | 1 | |a Lang, He | |
| 700 | 1 | |a Fang, Xi | |
| 700 | 1 | |a Xie, Liang | |
| 773 | 0 | |t Mathematics |g vol. 13, no. 3 (2025), p. 531 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3165831060/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3165831060/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3165831060/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |