Title: Singular Values and Singular Value Decomposition
Abstract: The spectral decomposition described in the preceding chapter is defined only for symmetric matrices, hence for square matrices. In this chapter, we extend it to arbitrary rectangular matrices; we define the “singular value decomposition,” which expresses any matrix in terms of its “singular values” and “singular vectors.” The singular vectors form a basis of the subspace spanned by the columns or the rows, defining a projection matrix onto it.
Publication Year: 2021
Publication Date: 2021-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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Cited By Count: 1
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