Singular Value Decomposition: Applications and Theorems

Related lectures (27)

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

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

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

Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.

Covers the topic of conformal transformations, including translations, dilations, rotations, and the conformal algebra.

Explains the step-by-step process of finding the singular value decomposition of a matrix.

Covers bases, transformations, and matrix decompositions in linear algebra.

Covers the concept of singular values in linear algebra and their properties, including diagonalization and practical examples.

Explores spectral and singular value decompositions of matrices.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.