PCA, SVD, and Centering of Data

arXiv:2307.15213v2 Announce Type: replace-cross
Abstract: The research detailed in this paper scrutinizes Principal Component Analysis (PCA), a seminal method employed in statistics and machine learning for the purpose of reducing data dimensionality. Singular Value Decomposition (SVD) is often employed as the primary means for computing PCA, a process that indispensably includes the step of centering - the subtraction of the mean location from the data set. In our study, we delve into a detailed exploration of the influence of this …

abstract analysis arxiv computing data dimensionality machine machine learning paper pca process research singular statistics stat.me stat.ml svd type value