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From: "Bruno Luong" <b.luong@fogale.findmycountry>
Newsgroups: comp.soft-sys.matlab
Subject: Re: how to deal with the inversion problem of a huge sparse
Date: Sun, 27 Sep 2009 12:40:18 +0000 (UTC)
Organization: FOGALE nanotech
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Rune Allnor <allnor@tele.ntnu.no> wrote in message <ace8cc81-2737-4630-83b6-76c09f427a97@p23g2000vbl.googlegroups.com>...

> 
> I'm a bit curious what kinds of situations occur where you
> need to handle a huge sparse covariance matrix.

many situations:

- data are organized in clusters and variables are only correlated within a clusters.
- two data are correlated with they distance (from a certain metric) is smaller than some threshold, and zero otherwise.
...

> 
> First of all, most processes of practical interest are
> stationary. You don't need the long-term covariances,
> which means that you can get away with a smaller covariance
> matrix.

You seem to infer *temporal* dimension in the OP problem. Aren't probably mistaken between correlation (time is no needed) and cross/auto-correlation ?

Bruno