Lecture

SVD: Singular Value Decomposition

Description

This lecture covers the concept of Singular Value Decomposition (SVD), which is used to compress information. The instructor explains how SVD can be applied to matrices, particularly in the context of compressing image data. The lecture delves into the mathematical foundations of SVD, discussing the properties of orthogonal matrices and diagonal matrices. Various examples are provided to illustrate the application of SVD in compressing information and its relevance in different scenarios.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_5bs1xuuu

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Ontological neighbourhood

Mathematics

Algebra: Linear algebra

Information engineering

Signal processing: Data compression, Digital image processing

Physics

Mechanics: Fluid mechanics

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