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*Subject*: Re: singular value decompostion*From*: "H T Onishi" <htonishi(at)home.com>*Date*: Fri, 02 Jul 1999 02:47:54 GMT*Newsgroups*: comp.lang.idl-pvwave*Organization*: @Home Network*References*: <377AD107.745DA866@home.com> <IVCe3.7424$0B1.5591@news.rdc2.occa.home.com> <377B78B4.F0C3ED30@home.com>*Xref*: news.doit.wisc.edu comp.lang.idl-pvwave:15516

> Thanks, that clarifies some points. You were correct, I copied down a .46 > rather than a -.46. > > I will run through your exercise to make sure I understand correctly. > > Do you have any experience with Principal component analysis and how that can > be done with SVD? > > Thanks again, > > Dave > I am not an expert in the SVD algorithm. The note by Fogel should be heeded and you may want to search the appropriate newsgroup for more info. I believe that the SVD algorithm implemented in IDL was taken from Numerical Recipes and there seems to be a lot of controversy over those routines. You may want to read http://math.jpl.nasa.gov/nr/nr-alt.html for more info. I know that I had to "tweak" an early version of the SVD routine from Numerical Recipes to get it to work properly -- problems with underflow. Regarding Principal Component Analysis, I believe that SVD is the key algorithm used to extract the PC's. I know that it has been used with some success to remove background clutter from imagery that contains moving targets. Again I am not an expert here (in fact not even a novice) but from what I understand a set of images of the same scene -- possibly multi-spectral -- is combined into a large matrix. Each image is turned into a vector and the set of vectors is combined into a large matrix. These vectors span a vector space and the PCs corresponding to the largest singular values represent clutter vectors that can then be subtracted from the original image. I think the PCs are in the V matrix, but there is a 50% chance that I'm wrong about that. That's about all I know. I'm sure there are many more applications. I suggest you do a search with AltaVista to see what you can find! Or you can be more conventional and do a literature search, which might be more fruitful. Howard

**References**:**singular value decompostion***From:*Dave Bazell

**Re: singular value decompostion***From:*H T Onishi

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