Hi Christian.
Do you think scikit-learn would be a more appropriate place
for such a 1d convolutional sparse coding than scipy.signal
or a possible scikit-signal
<http://brocabrain.blogspot.de/2012/01/scikit-signal-python-for-signal.html>?
I can not really judge. What do others think?
I would assume that doing a 1d convolutional version
of the sparse coding would need going down to the metal,
but I guess it should be possible to adapt existing code
- though I am really not familiar with the code.
Andy
Am 02.10.2012 11:05, schrieb Christian Vollmer:
Hi Andy,
I'm actually thinking about 1d convolution. I'd like to do sparse coding
on 1D multivariate signals. So far I used my own code, but I was hoping
to find better optimized and well-maintained implementations in
scikit-learn.
I think it would be nice to have some 1d convolutional algorithms in
scikit learn, since they are heavily used in signal processing for
audio, bio, and movement signals. I would even give it a try on my own.
But I guess it would not be as simple as subclassing some extisting
implementations and do some minor modifications, since the code in
scikit-learn seems pretty much optimized. Or is it?
Am 28.09.2012 14:29, schrieb Andreas Mueller:
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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