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Lecture 20 SVD and Its Applications

Lecture 20 SVD and Its Applications. Shang-Hua Teng. Spectral Theorem and Spectral Decomposition. Every symmetric matrix A can be written as. where x 1 … x n are the n orthonormal eigenvectors of A, they are the principal axis of A. x i x i T is the projection matrix on to x i !!!!!.

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Lecture 19 Singular Value Decomposition

Lecture 19 Singular Value Decomposition. Shang-Hua Teng. Spectral Theorem and Spectral Decomposition. Every symmetric matrix A can be written as. where x 1 … x n are the n orthonormal eigenvectors of A, they are the principal axis of A.

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784 views • 15 slides

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