Lecture 29: Singular Value Decomposition

author: Gilbert Strang, Department of Mathematics, Center for Future Civic Media, Massachusetts Institute of Technology, MIT
recorded by: Massachusetts Institute of Technology, MIT
published: Feb. 10, 2009,   recorded: November 1999,   views: 17433
released under terms of: Creative Commons Attribution Non-Commercial Share Alike (CC-BY-NC-SA)

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Reviews and comments:

Comment1 Richard, February 7, 2010 at 7:04 a.m.:

SVD is very useful and can be applied in many fields such as Computer vision and Pattern recognitions. The Strang's lecture on this topic is a comprehensive description of SVD. You just need some basic linear algebra knowledge to get it.
It is easily understandable and informative.

Comment2 Chris, April 5, 2010 at 3:14 a.m.:

The correct Eigenvektor in the first example is [-1 1]T only then to you get [-18 18]T = 18[-1 1]T as a result. With [1 -1]T you get [18 18]T = 18[1 1]T instead of 18[1 -1]T. Thats the mistake.

Great resource anyway. My math lectures should've been available as video, because I really need to stop from time to time. Everyone has his own thinking speed :-).

Comment3 Rainer Semma, February 2, 2011 at 11:31 p.m.:

Another mistake.
For A={{4,3},{8,6} you forgot to normalize V^T. So V^T={{4/5,3/5},{3/5,-4/5}}.

Comment4 Arshad Iqbal, September 22, 2011 at 12:25 p.m.:

Prof: Gilbert Strang thanks alot, you have no idea how much your lectures has helped me, keep up the good work.

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