Compressed Sensing: Mathematical Formulation

Ғылым және технология

This video introduces the mathematical theory of compressed sensing, related to high-dimensional geometry, robust statistics, and optimization.
Book Website: databookuw.com
Book PDF: 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: www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098/
Brunton Website: eigensteve.com
This video was produced at the University of Washington

Пікірлер: 38

  • @petercinque1421
    @petercinque14213 жыл бұрын

    Once again you are providing an excellent high level overview in very simple terms (for the mathematically trained). Really enjoy your videos.

  • @jimlbeaver
    @jimlbeaver4 жыл бұрын

    I have never come across this...what a powerful idea! You are doing a great job covering it. Thx

  • @prashantsharmastunning

    @prashantsharmastunning

    3 жыл бұрын

    2 months ago how?

  • @arturoenriquejasogarduno3022
    @arturoenriquejasogarduno30222 жыл бұрын

    I'm currently studying a Phd and your work and videos really inspire. Thanks for this great video series!

  • @cnbrksnr
    @cnbrksnr3 жыл бұрын

    You are a legend steve. I dont care about this method but still watch it because you make it interesting and understandable

  • @luisgg9496
    @luisgg94963 жыл бұрын

    Today is a GREAT day! Profesor Brunton upload a new vid :)

  • @AntiProtonBoy
    @AntiProtonBoy3 жыл бұрын

    I've been reading up on compressive sensing literature (e.g. by Richard Baraniuk et al.), and they are really hard follow for a math lightweight like me. Your explanations are so much clearer. Looking forward to see more in this series.

  • @stefanofiscale328
    @stefanofiscale3283 жыл бұрын

    Thank you for all your videos. This is how all professors should give a lecture at university.

  • @franciscojavierramirezaren4722
    @franciscojavierramirezaren47223 жыл бұрын

    Thanks a lot! Cant wait for next lecture! Greetings from México 🙂

  • @tasnimsarker4653
    @tasnimsarker46539 ай бұрын

    Thank you so much for making this. This helped me a lot. Please make more videos on compressive sensing. 😊

  • @prashantsharmastunning
    @prashantsharmastunning3 жыл бұрын

    great!!! cant wait for the next lecture.

  • @chaiyonglim
    @chaiyonglim3 жыл бұрын

    Love this topic series

  • @tinkeringengr
    @tinkeringengr3 жыл бұрын

    Love this guy!

  • @14_Phoenix
    @14_Phoenix Жыл бұрын

    Hi Professor, had a question regarding the equivalent formulation. Could we also reformulate the original convex problem as minimization of (L2 norm) ||Θs-y|| subject to constraint (L1 norm) ||s||

  • @heyjianjing
    @heyjianjing3 жыл бұрын

    Hi Professor, I have a question about the measurement matrix C. I see in literature that most seem to portrait it as a dense random matrix, not a spiky one on each row as you show here. So I guess you show C as a spiky matrix, just because that it is incoherent with the Fourier basis so it would function as well as a dense random matrix? I think I was confused originally when I saw you (in one of the previous video) taking random data points in time domain for super-positioned sine-waves, rather than taking random "combination" of all data points in time domain (which a random matrix would do). So, hopefully my above understanding is correct. Thanks for all the videos!

  • @1985lama
    @1985lama2 жыл бұрын

    is x here represents the compressed version of the original image since we are inferring the "active" Fourier coefficients? So it shouldn't be the high-resolution image. Am I correct?

  • @renganathansidharth
    @renganathansidharth3 жыл бұрын

    Love it! Why Does anyone need Netflix :)

  • @aminkh1845
    @aminkh18453 жыл бұрын

    Is the sparsest solution to the underdetermined problem unique?

  • @matteosavazzi7849
    @matteosavazzi78493 жыл бұрын

    Good morning Professor, Thank you for the nice videos. I have one question though: why do we want "the sparsest" s to be our solution? Shouldn't we look just for the "right" s? How can we claim that the sparsest s is the right one? Thank you, Matteo

  • @Eigensteve

    @Eigensteve

    3 жыл бұрын

    Great question. We want the sparsest vector because we have the observation that signals in nature are almost always very sparse. So solving for the sparsest vector is often a proxy for solving for the "natural" vector. This is extremely peculiar, and not at all obvious at first.

  • @paperexplained
    @paperexplained2 жыл бұрын

    but what if we have s and we want to find the right fourier basis?

  • @abhinavgupta9990
    @abhinavgupta99903 жыл бұрын

    Extremely relevant insights into the topic, professor. Thank you for discussing these.

  • @MrNeytrall
    @MrNeytrall3 жыл бұрын

    Is this somehow connected to LASSO and Ridge regressions? Love you videos! Thanks a lot!

  • @firemario876

    @firemario876

    3 жыл бұрын

    yea, lasso is analogous to L1 regularization, which encourages sparse answers stats.stackexchange.com/questions/200416/is-regression-with-l1-regularization-the-same-as-lasso-and-with-l2-regularizati

  • @weradsaoud2018
    @weradsaoud20185 ай бұрын

    Hello, thank you for this great lecture. I have a question, in the equation (y=C.x ) isn't possible that there are many xs that give the same y? Thank you in advance.

  • @weradsaoud2018

    @weradsaoud2018

    5 ай бұрын

    does the sparsest s constraint is sufficient to determine the wanted x?

  • @shreyadeore4784
    @shreyadeore47843 жыл бұрын

    Thanks for this video ok

  • @dabulls1g
    @dabulls1g3 жыл бұрын

    1:45 do you mean underdetermined or undetermined?

  • @Eigensteve

    @Eigensteve

    3 жыл бұрын

    Underdetermined... I wrote it wrong on the board.

  • @dabulls1g

    @dabulls1g

    3 жыл бұрын

    @Steve Brunton thanks! Great lecture!

  • @nivithpmuraliNSR
    @nivithpmuraliNSR3 жыл бұрын

    SIR COULD MAKE VIDEO ON USING KALMAN FILTER WITH C++ LANGUAGE OR PYTHON

  • @shirishavissom129
    @shirishavissom1293 жыл бұрын

    Hey please add the code for image compression using DCT,FFT,Wavelet

  • @Brainwizard.2

    @Brainwizard.2

    3 жыл бұрын

    Do your homework

  • @Ourfairduke
    @Ourfairduke2 жыл бұрын

    I appreciate you making videos on this topic for the not-so-bright people like me. I'm fine with utilizing math, but when that math is presented without context it drains all life out of me.

  • @navidseifosadat4020
    @navidseifosadat40202 жыл бұрын

    Thank you very much for your complete and helpful explanation. If possible, I would like to have your email and ask you some questions.

  • @saadimaster5961
    @saadimaster59612 жыл бұрын

    عاشت ايدك

  • @tommy1273
    @tommy12733 жыл бұрын

    I can't work out the transformation that he is using to write on the board 🤣. He's writing in reverse, right?!! :-O

  • @kristinacollins

    @kristinacollins

    3 жыл бұрын

    If I'm not mistaken, it's mirror-flipped and he's actually writing with his left hand.