Robust Regression with the L1 Norm [Matlab]

This video discusses how least-squares regression is fragile to outliers, and how we can add robustness with the L1 norm. (Code in Matlab)

Book Website: http://databookuw.com
Book PDF: http://databookuw.com/databook.pdf

These lectures follow Chapter 3 from:
“Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control” by Brunton and Kutz

Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098/

Brunton Website: eigensteve.com

source by Steve Brunton

matlab

Mourad ELGORMA

Fondateur de summarynetworks, passionné des nouvelles technologies et des métiers de Réseautique , Master en réseaux et système de télécommunications. ,j’ai affaire à Pascal, Delphi, Java, MATLAB, php …Connaissance du protocole TCP / IP, des applications Ethernet, des WLAN …Planification, installation et dépannage de problèmes de réseau informatique……Installez, configurez et dépannez les périphériques Cisco IOS. Surveillez les performances du réseau et isolez les défaillances du réseau. VLANs, protocoles de routage (RIPv2, EIGRP, OSPF.)…..Manipuler des systèmes embarqués (matériel et logiciel ex: Beaglebone Black)…Linux (Ubuntu, kali, serveur Mandriva Fedora, …). Microsoft (Windows, Windows Server 2003). ……Paquet tracer, GNS3, VMware Workstation, Virtual Box, Filezilla (client / serveur), EasyPhp, serveur Wamp,Le système de gestion WORDPRESS………Installation des caméras de surveillance ( technologie hikvision DVR………..). ,

8 réflexions sur “Robust Regression with the L1 Norm [Matlab]

  • juin 21, 2021 à 2:20
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    This is so amazing. Can you please hint at how you visualize those formulas. Do you have a glass in front of you or is it all in computer?

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  • juin 21, 2021 à 2:20
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    Could you collaborate with Robert van De Geijin and Maggie Myers at UT Austin? They have beautifully organized set of courses on Linear Algebra (Numerical Analysis with Computation). Your videos with their organization would be something else.

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  • juin 21, 2021 à 2:20
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    I'm really new to this stuff so I hope that this isn't an asinine question. Can you just throw out outliers if you can justify that they're rubbish data points? Is the advantage of this approach that you aren't required to justify the removal of outliers (and all the work that would go into that)?

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  • juin 21, 2021 à 2:20
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    Yes, I guess that L1 also becomes more popular due to the increase of available computing power. L2 still provides a fast analytical solution. L1 has to be iteratively minimized which can be way more inefficient than just analytically solving least squares (lots of data points and high dimensional fit). I found that out the hard way when I bought compute time to fit atomic partial charges to electrostatic potentials using L1.

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