Singular Value Decomposition: Matrices Transformation

In course

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Description

This lecture covers the Singular Value Decomposition (SVD) of matrices, explaining how any matrix can be decomposed into a sum of matrices using its eigenvalues and eigenvectors. The lecture details the process of transforming vectors through matrices, illustrating the concept with examples and discussing the implications of SVD in matrix operations.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_54vkg58s

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Ontological neighbourhood

Mathematics

Algebra: Linear algebra

Analysis: Tensor analysis

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