PCA and SVD are derived and compared on interpretability, numerical stability, and matrix shape suitability, yielding qualitative selection guidelines without empirical testing.
QRPCA: A Pack- age for Fast Principal Component Anal- ysis with GPU Acceleration,
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A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques
PCA and SVD are derived and compared on interpretability, numerical stability, and matrix shape suitability, yielding qualitative selection guidelines without empirical testing.