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Eigenvalues: Singular Value Decomposition

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Synonyms

Proper Values; Singular value decomposition = Principal component analysis

Glossary

Matrix:

A rectangular tableau of numbers

Eigenvalues:

A set of numbers (real or complex) intrinsic to a given matrix

Eigenvectors:

A set of vectors associated to a matrix transformation

Singular Value Decomposition:

A specific decomposition of any given matrix, useful in matrix analysis and its applications

Definition

Eigenvalues and Eigenvectors

Given a square (n × n) matrix A, a (complex) number λ is called an eigenvalue of A if there exists a nonzero n-dimensional column vector X such that

$$ AX=\lambda X,\, X\ne 0. $$
(1)

A vector X satisfying (1) is called an eigenvector of A corresponding to eigenvalue λ.

Singular Value Decomposition (SVD)

Given any rectangular matrix (m × n) matrix A, by singular value decomposition of the matrix A, we mean a decomposition of the form A = U Σ V T, where U and V are orthogonal matrices (representing rotations) and Σ is a diagonal matrix (representing a...

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Correspondence to Radu C. Cascaval .

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Cascaval, R.C. (2018). Eigenvalues: Singular Value Decomposition. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_142

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