Not a bad idea at all. The objective function is probably very asymmetrical when expressed with the value lambda. Transforming lambda might help with that. The asymmetry shouldn't be all that big a deal if you put a constrained 1-d optimizer on the problem.
On Fri, Dec 16, 2011 at 10:50 AM, Raphael Cendrillon < cendrillon1...@gmail.com> wrote: > Hi Dmitry, > > I have a feeling the objective may be very close to convex. In that case > there are faster approaches than random subsampling. > > A common strategy for example is to fit a quadratic onto the previously > evaluated lambda values, and then solve it for the minimum. > > This is an iterative approach, so wouldn't fit well within map reduce, but > if you are thinking of doing this as a preprocessing step it would be OK. > > On Dec 16, 2011, at 10:05 AM, Dmitriy Lyubimov <dlie...@gmail.com> wrote: > > > Hi, > > > > I remember vaguely the discussion of finding the optimum for reg rate > > in ALS-WR stuff. > > > > Would it make sense to take a subsample (or, rather, a random > > submatrix) of the original input and try to find optimum for it > > somehow, similar to total order paritioner's distribution sampling? > > > > I have put ALS with regularization and ALS-WR (and will put the > > implicit feedback paper as well) into R code and i was wondering if it > > makes sense to find a better guess for lambda by just doing an R > > simulation on a randomly subsampled data before putting it into > > pipeline? or there's a fundamental problem with this approach? > > > > Thanks. > > -Dmitriy >
