Uploaded May 2021 | Updated September 2026, 1 day ago
Want to know what Dynamic Mode Decompositions are? This video gives an introduction to dynamic mode decomposition (DMD) in signal processing. We compare the method with other methods, such as Fourier Series and Principle Component Analysis (PCA), where we see that DMD gives custom data driven bases for your signal, while also exploiting the dynamic content of your signal.
Music:
Come 2gether by Ooyy
Video Call from Los Angeles by Trevor Kowalski
Dismantle by Peter Sandberg
Wrong by Dan Henig
Guardians + Tek by Craig Hardgrove
0:00 Start and Introduction
1:26 DMD Algorithm
2:12 Fourier Series Discussion
3:54 PCA Discussion
5:12 Decompositions for Linear Systems (The Goal)
9:06 Koopman Operators and DMD
13:57 Wrap up
The Face Dataset was from the standard Yale Eigenface data as found at databookuw.com
Want to know what Dynamic Mode Decompositions are? This video gives an introduction to dynamic mode decomposition (DMD) in signal processing. We compare the method with other methods, such as Fourier Series and Principle Component Analysis (PCA), where we see that DMD gives custom data driven bases for your signal, while also exploiting the dynamic content of your signal.
Music:
Come 2gether by Ooyy
Video Call from Los Angeles by Trevor Kowalski
Dismantle by Peter Sandberg
Wrong by Dan Henig
Guardians + Tek by Craig Hardgrove
0:00 Start and Introduction
1:26 DMD Algorithm
2:12 Fourier Series Discussion
3:54 PCA Discussion
5:12 Decompositions for Linear Systems (The Goal)
9:06 Koopman Operators and DMD
13:57 Wrap up
The Face Dataset was from the standard Yale Eigenface data as found at databookuw.com


![Getting caught with facial recognition - How SVDs are used in Facial Recognition Software
This video builds on the SVD concepts of the previous videos, where I talk about the algorithm from the paper Eigenfaces for Recognition. These tools are used everywhere from law enforcement (such as tracking down the rioters at the Capitol) to unlocking your cell phone.
Followup Video: https://youtu.be/WnsKGBy1PXQ
Music:
Come 2gether by Ooyy
Guardians + Tek by Craig Hardgrove
Images:
Lectern Photo by Win McNamee/Getty Images (Used under Fair Use for an educational video)
Face Data obtained from http://www.databookuw.com/ the readme states
This data is modified from the Extended Yale Face Database B. If using this data, please also cite the following two papers:
[1] A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, From few to many: Illumination cone models for face recognition under variable lighting and pose, IEEE Trans. Pattern Anal. Mach. Intell., 23 (2001), pp. 643–660.
[2] K. Lee, J. Ho, and D. J. Kriegman, Acquiring linear subspaces for face recognition under variable lighting, IEEE Trans. Pattern Anal. Mach. Intell., 27 (2005), pp. 684–698.
Link to website: vision.ucsd.edu/content/extended-yale-face-database-b-b Getting caught with facial recognition - How SVDs are used in Facial Recognition Software](https://i.ytimg.com/vi/b-5jg5VvUEo/mqdefault.jpg)







