Explaining Singular Value Decomposition

Tim Bock explains how Singular Value Decomposition works:

The table above is a matrix of numbers. I am going to call it Z. The singular value decomposition is computed using the svd function. The following code computes the singular value decomposition of the matrix Z, and assigns it to a new object called SVD, which contains one vector, d, and two matrices, u and v. The vector, d, contains the singular values. The first matrix, u, contains the left singular vectors, and vcontains the right singular vectors. The left singular vectors represent the rows of the input table, and the right singular vectors represent their columns.

Tim includes R scripts to follow along, and for this topic I definitely recommend following along.

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