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Enhanced Multilinear PCA for Efficient Image Analysis and Dimensionality Reduction: Unlocking the Potential of Complex Image Data

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Vydáno v:Mathematics vol. 13, no. 3 (2025), p. 531
Hlavní autor: Sun, Tianyu
Další autoři: Lang, He, Fang, Xi, Xie, Liang
Vydáno:
MDPI AG
Témata:
Accuracy
Data analysis
Face recognition
Image analysis
Principal components analysis
Singular value decomposition
Datasets
Support vector machines
Image segmentation
Artificial neural networks
Task complexity
Tensors
Decomposition
Image classification
Traffic flow
Algorithms
Methods
Eigenvalues
Image processing
Dimensional analysis
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Abstrakt: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.
ISSN:2227-7390
DOI:10.3390/math13030531
Zdroj:Engineering Database