Singular Value Decomposition, Pseudoinverse

Related lectures (27)

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

Covers Singular Value Decomposition, focusing on its application in image compression and data representation.

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

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

Covers linear systems, diagonal and triangular matrices, and LU factorization.

Covers the Tucker decomposition and its applications in data compression, explaining the notion of multilinear rank and the HOSVD method.

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

Covers the construction of an iterative method for linear systems, emphasizing matrix decomposition and convexity.

Covers the formulation of linear systems, direct and iterative methods for solving them, and the cost of LU factorization.

Explores the Cholesky factorization method for symmetric positive definite matrices.