Hi Christian.
Are you thinking about 1d or 2d convolutions?
I am not so familiar with 1d signal processing but there has
been some work on convolutional sparse coding for image patches.
This is not really planned for sklearn, afaik, though.
In computer vision, I think there was no big difference in recognition
performance between convolutionally learned codes and codes
that where trained on patches and applied independently.
Hth.
Andy
Am 28.09.2012 10:59, schrieb Christian Vollmer:
> Hello,
>
> there is a nice collection of sparse coding and dictionary algorithms
> implemented in scikit-learn. However, it seems there are no
> shift-invariant implementations. Are there plans to include any
> shift-invariant implementations or is there a way to apply the
> implemented algorithms in a shift-invariant manner?
>
> With "shift-invariant" I mean a formulation, where the reconstruction is
> done by convolution of the atoms with the coefficients, like
> min_{U,V}||X-conv(U,W)||_2^2|| + alpha ||U||_1
>
> I think, I could get shift-invariant behaviour in the coding part by
> first building a dictionary of all shifted versions of all atoms and
> then apply the implemented sparse coding algorithms. However, I don't
> see a shift-invariant way for the dictionary learning part.
>
> Thanks,
> Christian
>
>
>
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