Singular Value Decomposition: Fundamentals and Applications

Related lectures (24)

Page 1 of 3

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 orthogonality, scalar products, orthogonal bases, and vector projection in detail.

Covers the theorems related to orthogonal projection and orthonormal bases.

Introduces orthogonal bases, projection onto subspaces, and the Gram-Schmidt process in linear algebra.

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

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

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

Explores spectral and singular value decompositions of matrices.

Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.