![]() These methods visualize both column and row information of a two-way matrix simultaneously, relate the matrix to relevant curves, and show local variations and interactions between columns and rows. Several matrix views of the SVD components are introduced to explore different features in data, including SVD surface plots, rotation movies, curve movies and image plots. ![]() ![]() A generalized scree plot is proposed in Section as a visual aid for model selection. In Section 2.3.2, we compare SVD and PCA from an FDA view point, and extend the usual SVD to potentially useful variations by considering different centerings. Compared to Principal Component Analysis (PCA), SVD is more general, because SVD simultaneously provides the PCAs in both the row and the column spaces. 1 Chapter 2 SVD, PCA and Visualizations Singular Value Decomposition (SVD) of a data matrix is a useful tool in Functional Data Analysis (FDA).
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