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Re: Fiinding standard deviation of a 3D point set
Posted:
Oct 4, 2012 12:41 PM


On Oct 3, 11:08 pm, "Anusha " <anu...@cs.usm.my> wrote: > TideMan <mul...@gmail.com> wrote in message <663ae35314824752950f74ce186064d9@googlegroups.com>... > > On Thursday, October 4, 2012 3:45:12 PM UTC+13, Anusha wrote: > > > Hi, > > > > I have a mean point set, represented by 3D coordinates(x,y,z) in 3D matrix. There a another point set in 3D coordinates. How can I compute the standard deviation between the mean and another point set? I want to know how much the dispersion of the given point set from the mean point set. > > > > Thanks. > > > Not sure I understand exactly what you're getting at, but how about: > > std(S1(:)S2(:)); > > where S1 and S2 are the 3D matrices. > > But S1 and S2 have different number of size. Its like S1 is Mx3 and S2 is Nx3.
This is just nearest mean clustering.
S1 are M cluster means S2 are N points to be assigned to clusters
So all you have to do is assign each member of S2 to the closest member of S1.
The rest is straight forward.
Hope this helps.
Greg



