Lecture

Singular Value Decomposition: Applications and Theorems

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In course

MATH-111(d): Linear Algebra

L'objectif du cours est d'introduire les notions de base de l'algèbre linéaire et ses applications. ...

Description

This lecture covers the Singular Value Decomposition (SVD) in linear algebra, focusing on its applications and theorems. It explains the concept of SVD, the decomposition into singular values, and the properties of the resulting matrices. The instructor discusses the use of SVD in image processing, matrix rotation, and symmetry. The lecture also delves into the Perron-Frobenius theorem, stability of eigenvectors, and the PageRank algorithm. Additionally, it explores the application of SVD in predicting weather patterns and analyzing transition probabilities in web page popularity. The lecture concludes with practical examples and real-world applications of SVD in various fields.

Instructors (2)

Giordano Favi

Expertise Research: Non associative Algebra, Category Theory, Triangulated Category Theory, Balmer-Witt Theory, Quadratic Forms in Characteristic 2, Galois Theory, Essential dimension, Tensor Triangular GeometryTeaching: Algebra (Associative, Linear, Commutative or not), Differential Calculus , Algebraic Geometry/Topology, etc. Current work Teaching: My current work at EPFL is half time dedicated to teaching mathematics for 1st year students. It is very important to give satisfactory introductory courses

Donna Testerman

Teaching & PhD Past EPFL PhD Students Soumaïa Nadia Ghandour, Iulian Ion Simion, Mikaël Cavallin, Mikko Tapani Korhonen, Nathan Scheinmann, Ana Retegan, Jonathan Gruber

Official source

Ontological neighbourhood

Mathematics

Algebra: Linear algebra

Related concepts (30)

Singular value decomposition

In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any matrix. It is related to the polar decomposition. Specifically, the singular value decomposition of an complex matrix M is a factorization of the form where U is an complex unitary matrix, is an rectangular diagonal matrix with non-negative real numbers on the diagonal, V is an complex unitary matrix, and is the conjugate transpose of V.

Rotation matrix

In linear algebra, a rotation matrix is a transformation matrix that is used to perform a rotation in Euclidean space. For example, using the convention below, the matrix rotates points in the xy plane counterclockwise through an angle θ about the origin of a two-dimensional Cartesian coordinate system. To perform the rotation on a plane point with standard coordinates v = (x, y), it should be written as a column vector, and multiplied by the matrix R: If x and y are the endpoint coordinates of a vector, where x is cosine and y is sine, then the above equations become the trigonometric summation angle formulae.

Eigenvalues and eigenvectors

In linear algebra, an eigenvector (ˈaɪgənˌvɛktər) or characteristic vector of a linear transformation is a nonzero vector that changes at most by a constant factor when that linear transformation is applied to it. The corresponding eigenvalue, often represented by , is the multiplying factor. Geometrically, a transformation matrix rotates, stretches, or shears the vectors it acts upon. The eigenvectors for a linear transformation matrix are the set of vectors that are only stretched, with no rotation or shear.

QR decomposition

In linear algebra, a QR decomposition, also known as a QR factorization or QU factorization, is a decomposition of a matrix A into a product A = QR of an orthonormal matrix Q and an upper triangular matrix R. QR decomposition is often used to solve the linear least squares problem and is the basis for a particular eigenvalue algorithm, the QR algorithm. Any real square matrix A may be decomposed as where Q is an orthogonal matrix (its columns are orthogonal unit vectors meaning ) and R is an upper triangular matrix (also called right triangular matrix).

Singular value

In mathematics, in particular functional analysis, the singular values, or s-numbers of a compact operator acting between Hilbert spaces and , are the square roots of the (necessarily non-negative) eigenvalues of the self-adjoint operator (where denotes the adjoint of ). The singular values are non-negative real numbers, usually listed in decreasing order (σ1(T), σ2(T), ...). The largest singular value σ1(T) is equal to the operator norm of T (see Min-max theorem).

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