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From: Rune Allnor <allnor@tele.ntnu.no>
Newsgroups: comp.soft-sys.matlab
Subject: Re: how to deal with the inversion problem of a huge sparse 
	covariance matrix
Date: Sun, 27 Sep 2009 11:05:55 -0700 (PDT)
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On 27 Sep, 19:35, "Bruno Luong" <b.lu...@fogale.findmycountry> wrote:
> "Hua Wang" <ehw...@163.com> wrote in message <h9o54a$pn...@fred.mathworks.com>...
>
> > I am not sure whether it is clear enough for the explanation of the covariance matrix. Sorry that my english is not good enough.
>
> Perfectly clear. You have actually confirmed the two cases that I assumed above where the covariance matrix is sparse - namely distance (within an image) and clusters (different images).
>
> I have no idea by which trick Rune want to employ to reduce it to a 10 x 10 matrix.

I haven't seen anything that indicates neither the size of
these images in terms of pixels, or the dimensions of the
covarinance matrices. Nor have I seen anything to indicate
what the purpose of the analysis is.

Again, any desire to invert a large matrix is almost always
wrong. In the few cases where inversion is warranted, the
naive inversion is almost never the correct algorithm.

Of course, it could be that the OP's application is among the
handful that remains, but it might be worth the effort to
investigate alternative algorithms.

Rune