An Overview of Compressed Sensing and Sparse Signal Recovery via L1 Minimization

author: Emmanuel Candes, Department of Statistics, Stanford University
published: July 30, 2009,   recorded: June 2009,   views: 79371

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In many applications, one often has fewer equations than unknowns. While this seems hopeless, the premise that the object we wish to recover is sparse or nearly sparse radically changes the problem, making the search for solutions feasible. This lecture will introduce sparsity as a key modeling tool together with a series of little miracles touching on many areas of data processing. These examples show that finding *that* solution to an underdetermined system of linear equations with minimum L1 norm, often returns the ''right'' answer. Further, there is by now a well-established body of work going by the name of compressed sensing, which asserts that one can exploit sparsity or compressibility when acquiring signals of general interest, and that one can design nonadaptive sampling techniques that condense the information in a compressible signal into a small amount of data - in fewer data points than were thought necessary. We will survey some of these theories and trace back some of their origins to early work done in the 50's. Because these theories are broadly applicable in nature, the tutorial will move through several applications areas that may be impacted such as signal processing, bio-medical imaging, machine learning and so on. Finally, we will discuss how these theories and methods have far reaching implications for sensor design and other types of designs.

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Reviews and comments:

Comment1 behrooz, October 23, 2009 at 11:08 a.m.:

Would you put the link to the presentation slides

Comment2 hnliu, October 29, 2009 at 7:27 a.m.:

Yes, I want that too.

Comment3 Luo Wei, December 17, 2009 at 4:38 a.m.:

The great overview about CS

Comment4 iman, December 24, 2009 at 2:39 a.m.:

we really need the slides,
thanks for the nice talk.

Comment5 Mazin, October 12, 2011 at 6:33 p.m.:

could you please upload sildes online

Comment6 shreeja , October 19, 2012 at 3:27 p.m.:

could i know, how to download this video lecture from the website.

Comment7 Chengyu Peng, November 9, 2012 at 2:31 p.m.:

It's a remarkable review!

Since the web does not always be fluent,would you give us a way to download the video lecture?

Comment8 tahira batool, January 22, 2013 at 10:15 p.m.:

its a great tutorial about CS but the point is same as that of Chengyu Peng,s that is there any way to download it if so plz guide as soon as possible

Comment9 abhishek, June 6, 2013 at 7:47 a.m.:

this is not the way to take a seminar bullshit

Comment10 Latha, January 18, 2014 at 2:52 p.m.:

Please tell me how to download

Comment11 m, March 2, 2015 at 2:15 p.m.:


Comment12 DGS, April 30, 2015 at 6:27 p.m.:

The handouts for Dr. Candes' Stats 330 course are helpful.

Comment13 Chunpai, January 5, 2016 at 9:07 p.m.:

Anyone can share the handouts from Dr. Candes' Stats 330 course ????? Not able to access any longer. Thanks ~

Comment14 Rihan, April 16, 2017 at 8:25 a.m.:

Please I want the slides/handouts of the course

Comment15 shraddha pandey, May 24, 2018 at 3:22 p.m.:

Can any one please share the slides and course hand outs. The link mentioned above is not accessible for all.

Thank you

Comment16 Emma, June 21, 2018 at 9:50 a.m.:

Need the note + 1
Could anyone who have the note share with me? My e-mail is Please contact me, thank you!

Comment17 Nang, June 27, 2022 at 2:46 p.m.:

How to download? download link needed.

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