Re: [Scikit-learn-general] Benchmarking non-negative least squares solvers, work in progress

2013-10-29 Thread Gael Varoquaux
On Wed, Oct 30, 2013 at 12:49:49AM +0100, Vlad Niculae wrote: > Adding "L1" (elementwise) regularization makes L-BFGS-B converge much > quicker. This is cool because for NMF such a penalty has other > advantages. By the way, the MiniBatchDictLearning can be trivially modified to do this: do a non

Re: [Scikit-learn-general] Image Feature Classification Conceptual Fog

2013-10-29 Thread Andreas Mueller
On 10/29/2013 07:19 AM, jim vickroy wrote: On 10/29/2013 5:11 AM, Olivier Grisel wrote: 2013/10/23 j vickroy: On 10/23/2013 10:18 AM, Andreas Mueller wrote: FYI, the features I would use for the superpixel based approach would be "color" histogramms (bag of words of channel intensities): Resha

[Scikit-learn-general] Benchmarking non-negative least squares solvers, work in progress

2013-10-29 Thread Vlad Niculae
Hi all, During the PyCon sprint I kept digging into the NMF and specifically ways to solve each sub-iteration. It became clear that the alternating NLS approach finds good reconstructions and converges well, but the NLS solving step is critical and must be optimized. I have started looking into d

Re: [Scikit-learn-general] Image Feature Classification Conceptual Fog

2013-10-29 Thread jim vickroy
On 10/29/2013 5:11 AM, Olivier Grisel wrote: 2013/10/23 j vickroy : On 10/23/2013 10:18 AM, Andreas Mueller wrote: FYI, the features I would use for the superpixel based approach would be "color" histogramms (bag of words of channel intensities): Reshape the images to (-1, 6) so you have lists

Re: [Scikit-learn-general] Image Feature Classification Conceptual Fog

2013-10-29 Thread Olivier Grisel
2013/10/23 j vickroy : > On 10/23/2013 10:18 AM, Andreas Mueller wrote: >> FYI, the features I would use for the superpixel based approach would be >> "color" histogramms (bag of words of channel intensities): >> Reshape the images to (-1, 6) so you have lists of pixels (subsample if >> they are ma

Re: [Scikit-learn-general] Image Feature Classification Conceptual Fog

2013-10-29 Thread j vickroy
On 10/23/2013 10:18 AM, Andreas Mueller wrote: > FYI, the features I would use for the superpixel based approach would be > "color" histogramms (bag of words of channel intensities): > Reshape the images to (-1, 6) so you have lists of pixels (subsample if > they are many), run (MiniBatch)KMeans, a

Re: [Scikit-learn-general] Python-Recsys/Crab using scikit-learn code without attribution (stealing?)

2013-10-29 Thread Lars Buitinck
2013/10/28 Marcel Caraciolo : > Thanks Lard and Jaques we are still at development. We are referencing some > distance metrics of Scikit-learn and unfortunatelly The author missed to > cross-reference the code for the scikit. Apologize ourserves, and we are > still working on it. People abandoned t