I misunderstood that your "random data" is resampled from original data.
btw, for transforming to orthogonal features:
http://scikit-learn.org/stable/auto_examples/decomposition/plot_ica_vs_pca.html#example-decomposition-plot-ica-vs-pca-py
2014-03-17 16:40 GMT+08:00 Caleb <cloverev...@yahoo.com>:
> >Hi, please repeat your experiments several times because both >Random
> Forest Embedding > and your resampling are ?>not deterministic algorithm.
> their results may varys a lot
> >please also check the attributes "n_support_" >and "support_vectors_" in
> the object, they're > highly related to >time complexity
>
> I do repeat the experiments several times and the results are similar. And
> there are no "support_vectors_" and "n_support_" attribute in the LinearSVC
> object.
>
> >LinearSVC converges faster with high regularization parameter ?
> >(small C)
> >and the speed depends on the conditioning of the problem. If you >had
> >orthogonal features it would be super fast. The more correlated >they
> are the slower it is.
>
>
> Thanks for mentioning that Alex. I should look into if my features are
> independent.
>
> I have also posted my notebook here for those who are interested
> http://nbviewer.ipython.org/gist/catethos/9595760
>
>
> ------------------------------------------------------------------------------
> Learn Graph Databases - Download FREE O'Reilly Book
> "Graph Databases" is the definitive new guide to graph databases and their
> applications. Written by three acclaimed leaders in the field,
> this first edition is now available. Download your free book today!
> http://p.sf.net/sfu/13534_NeoTech
> _______________________________________________
> Scikit-learn-general mailing list
> Scikit-learn-general@lists.sourceforge.net
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
------------------------------------------------------------------------------
Learn Graph Databases - Download FREE O'Reilly Book
"Graph Databases" is the definitive new guide to graph databases and their
applications. Written by three acclaimed leaders in the field,
this first edition is now available. Download your free book today!
http://p.sf.net/sfu/13534_NeoTech
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
