Investigating Innovative Adaptations And Hybrid Models Of Matrix Decomposition For Enhanced Accuracy, Scalability, And Robustness In Real-World Engineering And Data-Driven Scenarios
Keywords:
Matrix Decomposition, Hybrid Models, SVD, PCA, ICA, NMF, Robustness, Scalability, Engineering, Data AnalysisAbstract
Matrix decomposition is foundational in modern data analysis and engineering, enabling dimensionality reduction, noise filtering, and feature extraction. However, traditional techniques such as Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) can face limitations in terms of scalability, accuracy, and robustness, particularly when applied to large, noisy, or non-linear datasets. This paper explores recent advances and hybrid adaptations of matrix decomposition, focusing on models that integrate multiple algorithms or introduce domain-specific innovations. Applications in image processing, signal analysis, and big data contexts are reviewed, with a discussion of the advantages and implementation strategies of these cutting-edge approaches.
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