Joint Approximation Diagonalization of Eigen-matrices (JADE) is an algorithm for independent component analysis that separates observed mixed signals into latent source signals by exploiting fourth order moments. The fourth order moments are a measure of non-Gaussianity, which is used as a proxy for defining independence between the source signals. The motivation for this measure is that Gaussian distributions possess zero excess kurtosis, and with non-Gaussianity being a canonical assumption of ICA, JADE seeks an orthogonal rotation of the observed mixed vectors to estimate source vectors which possess high values of excess kurtosis. (Wikipedia).
Abstract eigenvalues and eigenvectors In this video, I show how to find eigenvalues and eigenvectors of an abstract linear transformation, namely in this case the transformation on polynomials that switches the leading order and the constant term. Enjoy! Check out my Diagonalization play
From playlist Diagonalization
Example of Simultaneous Diagonalization
Matrix Theory: Find a joint eigenbasis for the commuting matrices A = [2 2 \ 2 2] and B = [1 2 \ 2 1]. That is, find a basis of eigenvectors that simultaneously diagonalize A and B.
From playlist Matrix Theory
matrix choose a matrix. Calculating the number of matrix combinations of a matrix, using techniques from linear algebra like diagonalization, eigenvalues, eigenvectors. Special appearance by simultaneous diagonalizability and commuting matrices. In the end, I mention the general case using
From playlist Eigenvalues
(4.1.3) Orthogonality of Eigenfunctions Theorem and Proof
This video explains and proves a theorem on the orthogonality of eigenfunctions. https://mathispower4u.com
From playlist Differential Equations: Complete Set of Course Videos
Find the Diagonalization of a 3 by3 Matrix Given Eigenvectors and Eigenvalues
This video explains how to complete the diagonalization of a 3 by 3 matrix given matrix the eigenvalues and corresponding eigenvectors.
From playlist The Diagonalization of Matrices
Matrix with complex eigenvalues and diagonalization. Featuring polar decomposition, which is like polar coordinates, but for matrices. Check out my Eigenvalues playlist: https://www.youtube.com/watch?v=H-NxPABQlxI&list=PLJb1qAQIrmmC72x-amTHgG-H_5S19jOSf Subscribe to my channel: https://w
From playlist Eigenvalues
Diagonalizing a 3x3 matrix. Finding eigenvalues and eigenvectors. Featuring the rational roots theorem and long division Check out my Eigenvalues playlist: https://www.youtube.com/watch?v=H-NxPABQlxI&list=PLJb1qAQIrmmC72x-amTHgG-H_5S19jOSf Subscribe to my channel: https://www.youtube.com
From playlist Eigenvalues
Random Matrix Theory And its Applications by Satya Majumdar ( Lecture - 1 )
PROGRAM BANGALORE SCHOOL ON STATISTICAL PHYSICS - X ORGANIZERS : Abhishek Dhar and Sanjib Sabhapandit DATE : 17 June 2019 to 28 June 2019 VENUE : Ramanujan Lecture Hall, ICTS Bangalore This advanced level school is the tenth in the series. This is a pedagogical school, aimed at bridgin
From playlist Bangalore School on Statistical Physics - X (2019)
Linear Algebra: Ch 3 - Eigenvalues and Eigenvectors (27 of 35) Diagonalization (Part 3/3)
Visit http://ilectureonline.com for more math and science lectures! In this video I will find the eigenvalue=? and the eigenvectors=? using diagonalization (2x2 matrix) (Part 3/3). Next video in this series can be seen at: https://youtu.be/Kok-WoOqQRU
From playlist LINEAR ALGEBRA 3: EIGENVALUES AND EIGENVECTORS
Universality in numerical computations with random data. Analytical results. - Percy Deift
Analysis Math-Physics Seminar Topic: Universality in numerical computations with random data. Analytical results. Speaker: Percy Deift Affiliation: New York University Date: October 19, 2016 For more video, please visit http://video.ias.edu
From playlist Mathematics
Thomas Mikosch : Asymptotic theory for the sample covariance matrix of a heavy-tailed [...]
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Probability and Statistics
Top eigenvalue of a Gaussian random matrix: Large Deviations by Satya Majumdar
Large deviation theory in statistical physics: Recent advances and future challenges DATE: 14 August 2017 to 13 October 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru Large deviation theory made its way into statistical physics as a mathematical framework for studying equilibrium syst
From playlist Large deviation theory in statistical physics: Recent advances and future challenges
Andy Wathen: Parallel preconditioning for time-dependent PDEs and PDE control
We present a novel approach to the solution of time-dependent PDEs via the so-called monolithic or all-at-once formulation. This approach will be explained for simple parabolic problems and its utility in the context of PDE constrained optimization problems will be elucidated. The underlyi
From playlist Numerical Analysis and Scientific Computing
Shiping Liu (7/29/22): Signed graphs and Nodal domain theorems for symmetric matrices
Abstract: A signed graph is a graph whose edges are labelled by a signature. It serves as a simple model of discrete vector bundle. We will discuss nodal domain theorems for arbitrary symmetric matrices by exploring the induced signed graph structure. This is an extension of the nodal doma
From playlist Applied Geometry for Data Sciences 2022
Universidad de la República, Uruguay Some Results on the Complexity of the Eigenvalue Problem In this talk we will focus on the complexity of some algorithms designed for the eigenvalue problem. In particular we will present an algorithm for the eigenvalue(eigenvector) problem which runs
From playlist Fall 2019 Symbolic-Numeric Computing Seminar
Random quantum marginals - Michael Walter
Michael Walter ETH March 31, 2014 For more videos, visit http://video.ias.edu
From playlist Mathematics
Math 060 Linear Algebra 28 111914: Diagonalization of Matrices
Diagonalization of matrices; equivalence of diagonalizability with existence of an eigenvector basis; example of diagonalization; algebraic multiplicity; geometric multiplicity; relation between the two (geometric cannot exceed algebraic).
From playlist Course 4: Linear Algebra
The circular law for sparse non-Hermitian random matrices by Anirban Basak
Speaker : Anirban Basak, Weizmann Institute of Science, Israel Date : Tuesday, October 10, 2017 Time : 4:00 PM Venue : Madhava Lecture Hall, ICTS Campus, Bangalore Abstract : Sparse matrices are abundant in statistics, neural network, financial modeling, electrica
From playlist ICTS Colloquia
Linear Algebra: Ch 3 - Eigenvalues and Eigenvectors (26 of 35) Diagonalization (Part 2/3)
Visit http://ilectureonline.com for more math and science lectures! In this video I will find the eigenvalue=? and the eigenvectors=? using diagonalization (2x2 matrix) (Part 2/3). Next video in this series can be seen at: https://youtu.be/dkv1z1uS8OI
From playlist LINEAR ALGEBRA 3: EIGENVALUES AND EIGENVECTORS