Hello,
Replacing the squared L2 norm by the L1 norm in the linear regression
gives a good robustness to outliers (cf. Donoho and al. papers). The
problem is then non differentiable but you can implement it by
iteratively reweighting the classical L2 method (IRLS method), or by
writing an equivalent linear program.
S.
Le 04/03/13 13:23, Dang, Christophe a écrit :
Hello,
De la part de Rafael Guerra
Envoyé : lundi 4 mars 2013 04:37
Does somebody know if there are Scilab functions
[...] that smooths
experimental data z=f(x,y) and is immune to strong outliers.
imho, the problem with smoothing and outliers is that
the definition of a outlier depends on the field.
How can Scilab know what a "strong outlier" is?
I personally would try Fourier filtering:
a strong outlier means a steep slope
and therefore correspond to a high frequency.
Thus fft2, set high frequencies to 0
(with possibly a smooth transition),
then inverse fft2 -- ifft2 does not exist, I never used 2-dimension
Fourier transform so I don't know if the inverse is easy to perform...
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