Singular Value Decomposition

Related lectures (24)

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Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

Explores Singular Value Decomposition, low-rank approximation, fundamental subspaces, and matrix norms.

Explains orthogonal families, bases, and projections in vector spaces.

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.

Covers the theorems related to orthogonal projection and orthonormal bases.

Covers the Singular Value Decomposition (SVD) in detail, including properties of matrices and system linearity.