11H Orthogonal Projection of a Vector
The orthogonal projection of one vector along another.
From playlist Linear Algebra
11J Orthogonal Projection of a Vector
The orthogonal projection of one vector along another.
From playlist Linear Algebra
11I Orthogonal Projection of a Vector
The Orthogonal Projection of one vector along another.
From playlist Linear Algebra
This is the first video of a linear algebra-series on orthogonality. In this video, I define the notion of orthogonal sets, then show that an orthogonal set without the 0 vector is linearly independent, and finally I show that it's easy to calculate the coordinates of a vector in terms of
From playlist Orthogonality
In this video, I define the concept of orthogonal projection of a vector on a line (and on more general subspaces), derive a very nice formula for it, and show why orthogonal projections are so useful. You might even see the hugging formula again. Enjoy! This is the second part of the ort
From playlist Orthogonality
Find an Orthogonal Projection of a Vector Onto a Line Given an Orthogonal Basis (R2)
This video explains how t use the orthogonal projection formula given subset with an orthogonal basis. The distance from the vector to the line is also found.
From playlist Orthogonal and Orthonormal Sets of Vectors
11L More Example of Dot Product and Orthogonal Projections
More example of the dot product and orthogonal projections.
From playlist Linear Algebra
Linear Algebra 7.1 Orthogonal Matrices
My notes are available at http://asherbroberts.com/ (so you can write along with me). Elementary Linear Algebra: Applications Version 12th Edition by Howard Anton, Chris Rorres, and Anton Kaul A. Roberts is supported in part by the grants NSF CAREER 1653602 and NSF DMS 2153803.
From playlist Linear Algebra
Stéphane Mallat: A Wavelet Zoom to Analyze a Multiscale World
Abstract: Complex physical phenomena, signals and images involve structures of very different scales. A wavelet transform operates as a zoom, which simplifies the analysis by separating local variations at different scales. Yves Meyer found wavelet orthonormal bases having better propertie
From playlist Abel Lectures
Lec 28 | MIT 18.085 Computational Science and Engineering I
Splines and orthogonal wavelets: Daubechies construction A more recent version of this course is available at: http://ocw.mit.edu/18-085f08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 18.085 Computational Science & Engineering I, Fall 2007
Wavelets and Multiresolution Analysis
This video discusses the wavelet transform. The wavelet transform generalizes the Fourier transform and is better suited to multiscale data. Book Website: http://databookuw.com Book PDF: http://databookuw.com/databook.pdf These lectures follow Chapter 2 from: "Data-Driven Science an
From playlist Data-Driven Science and Engineering
Glen Evenbly: "Using tensor networks to design improved wavelets for image compression"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop II: Tensor Network States and Applications "Using tensor networks to design improved wavelets for image compression" Glen Evenbly - Georgia Institute of Technology, Physics Abstract: Tensor networks
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
3. Orthonormal Columns in Q Give Q'Q = I
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Instructor: Gilbert Strang View the complete course: https://ocw.mit.edu/18-065S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k This lecture focus
From playlist MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
Angela Kunoth: 25+ Years of Wavelets for PDEs
Abstract: Ingrid Daubechies' construction of orthonormal wavelet bases with compact support published in 1988 started a general interest to employ these functions also for the numerical solution of partial differential equations (PDEs). Concentrating on linear elliptic and parabolic PDEs,
From playlist Numerical Analysis and Scientific Computing
Lec 30 | MIT 18.085 Computational Science and Engineering I, Fall 2008
Lecture 30: Discrete Fourier series License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 18.085 Computational Science & Engineering I, Fall 2008
Stéphane MALLAT - Mathematical mysteries of deep neural networks
https://ams-ems-smf2022.inviteo.fr/
From playlist International Meeting 2022 AMS-EMS-SMF
Orthogonality and Orthonormality
We know that the word orthogonal is kind of like the word perpendicular. It implies that two vectors have an angle of ninety degrees or half pi radians between them. But this term means much more than this, as we can have orthogonal matrices, or entire subspaces that are orthogonal to one
From playlist Mathematics (All Of It)
Lec 26 | MIT 18.085 Computational Science and Engineering I
Filter banks and perfect reconstruction A more recent version of this course is available at: http://ocw.mit.edu/18-085f08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 18.085 Computational Science & Engineering I, Fall 2007
Empirical Mode Decomposition (1D, univariate approach)
Introduction to the Empirical Mode Decomposition - EMD - (one-dimensional, univariate version), which is a data decomposition method for non-linear and non-stationary data. This video covers the main features of the EMD and the working principle of the algorithm. The EMD is briefly compar
From playlist Summer of Math Exposition Youtube Videos