Singular Value Decomposition and its Visualization

@inproceedings{Zhang2004SingularVD,
  title={Singular Value Decomposition and its Visualization},
  author={Lingsong Zhang and Zhengyuan Zhu},
  year={2004}
}
Singular Value Decomposition (SVD) is a useful tool in Functional Data Analysis (FDA). Compared to Principal Component Analysis (PCA), SVD is more fundamental, because SVD simultaneously provides the PCAs in both row and column spaces. We compare SVD and PCA from the FDA view point, and extend the usual SVD to variations by considering different centerings. A generalized scree plot is proposed to select an appropriate centering in practice. Several matrix views of the SVD components are… CONTINUE READING
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