we're clustering just to make KNN faster over a huge dataset so it's not 100% necessary at the moment
hierarchical clustering lets you do neat things like partition datasets when searching for recommendations/etc
@AtheMathmo dunno if you already saw, but it looks like someone scooped you on integrating with LAPACK: https://github.com/masonium/linxal
@AtheMathmo re: #145 After trying out some tests and benchmarks for the QR-based lin_reg training method I had, this implementation at least is probably not worth the speed trade-off (the cases where it makes a noticeable difference in parameters aren't super-realistic and it's much slower).
Although it should be noted that some large portion of the slow-down is likely due to my implementation, since the theoretical differences are not nearly so large.
It's possible that your approach has some stability advantages that might be worth looking into. I'm not too sure personally and would need to do some reading I imagine
@AtheMathmo it has some minor stability advantages in terms of the stability of the solution informal tests, but only in the really artificial case that I tried.
I was thinking to use GP
any suggestions?
this is a time series data.
i want to use NB classifier
but the problem is that i have a huge set, 2.3M document and 300k feature, it's stored in a sparse matrix, but the classifier only accepts dense matrix
