[SciPy-User] scipy.interpolate.rbf sensitive to input noise ?
Tue Feb 23 11:43:55 CST 2010
thanks much for taking the time to look at RBF some more.
Summary, correct me:
A - smooth*I in rbf.py is a sign error (ticket ?)
for gauss, start with A + 1e-6*I to move eigenvalues away from 0
others have pos/neg eigenvalues, don't need smooth.
Looking at Wikipedia Tikhonov (thanks Josef) reminded me
of lstsq_near on advice.mechanicalkern:
minimize |Ax-b| and w|x| together, i.e. Tikhonov with Gammamatrix = I.
But for RBF, why minimize |x| ? don't we really want to minimize
|Ax-b|2 + w2 xRx
where R is a roughness penalty ?
On a regular grid Rij ~ Laplacian smoother, not much like I.
Here I'm over my head;
any ideas for a q+d roughness matrix for scattered data ?
(just for fun -- for RBF we've reached the point of diminishing
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