> Also David experienced poor performance compared to other > implementation when using the CD models in a sparse coding. Would be > > You mean that the data matrix X has a lot of zero entries? There is a > comment > on this case in section 2.3 ( www.stanford.edu/~hastie/Papers/glmnet.pdf ).
section 2.3 is for sparse X. There a sparse coordinate descent too which btw does not support intercept if you feel brave :) the poor performance can be improved by adding strong rules and simply profiling eg making sure that data are Fortran-ordered when it's faster. > great to ensure comparable performance with state of the art for this > use case as well. Investigating with OpenMP via cython prange might be > possible solution. > > I'm not sure if the algorithm is good to parallelize, I think there a other > speed up tricks > not used yet but I will look into it too. good. Parallel computation can be particularly useful when dealing with many y's ie Y with many regression tasks run in parallel as in sparse_encode used in the dict learning code. > Thanks for the suggestions, I start drafting the proposal as soon as I'm > done with > the patch. perfect Cheers, A ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
