Could you be contaminating the cross product matrix when you do your
accumulation? I noticed one of the utility classes has methods for doing
safe accumulations. I know that this was part of the problem when I was
trying to estimate the Wampler data with the MillerRegression. Just a
thought...
On Thu, Sep 8, 2011 at 10:22 AM, Gilles Sadowski <
gilles@harfang.homelinux.org> wrote:
> On Thu, Sep 08, 2011 at 09:49:12AM 0500, Greg Sterijevski wrote:
> > Cholesky, in my opinion, is not robust as you have discovered. When it
> > encounters a nonpsd matrix it gives up. Maybe that is the correct course
> of
> > action, but I still think that when you are using the getCovariance to
> > estimate the curvature in the neighborhood of a point it would be okay to
> > take the generalized inverse and not worry too much about why your matrix
> is
> > bordering on nonPSDness... If your optimization stops on that point,
> that
> > is another story and should be flagged.
> >
>
> In fact, in my current case (which is a unit test), the point is supposed
> to
> be the minimum, by construction. I'm trying to figure out where the
> problem comes from (namely, whether the Jacobian matrix is correct)...
>
> Gilles
>
> >
> > > A possibly more robust option here is to use Cholesky decomposition,
> > > > which is known to be stable for symmetric positive definite
> > > > matrices, which the covariance matrix being inverted here should
> > > > be. The exceptions thrown will be different; but they will give
> > > > more specific information about what is wrong with the covariance
> > > > matrix.
> > >
> > > I've tried it with my problem, and it also throws an exception.
> > > However, I would like to obtain the covariance matrix anyway, because
> I've
> > > no other clue as to what might be wrong.
> > > So I think that, at least, users should be able to set the positive
> > > definiteness threshold in order to avoid raising an exception.
> > >
> > >
> > >
>
> 
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