Evaluating Compressed Sensing Matrix Techniques: A Comparative Study of PCA and Conventional Methods
DOI:
https://doi.org/10.26877/h26m6b34Keywords:
Compressed Sensing, Principal Component Analysis (PCA), Data Dimensionality Reduction, Signal Processing, Measurement Matrix, Image Compression, Signal Reconstruction Techniques, Data AnalyticsAbstract
This research examines the performance of various compressed sensing matrix techniques, with a focus on Principal Component Analysis (PCA) compared to conventional methods. By applying these techniques to a range of high-dimensional datasets, we assess their effectiveness in reducing data dimensionality while preserving essential information. Our results demonstrate that PCA consistently outperforms traditional methods in terms of both accuracy and computational efficiency. These findings have significant implications for fields such as signal processing, image compression, and data analytics, where efficient data representation is critical. The study provides a framework for selecting the optimal dimensionality reduction technique, enabling improvements in processing speed and accuracy in practical applications.
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