Anything that requires the solution of large linear systems is usually susceptible to SGD approaches.
On Tue, Sep 20, 2011 at 11:24 AM, deneche abdelhakim <[email protected]>wrote: > I was reading this paper: > > "Combining Predictions for Accurate Recommender Systems" > http://www.commendo.at/UserFiles/commendo/File/kdd2010-paper.pdf > > and one particular method used to blend different recommenders is KRR > (Kernel Ridge Regression). The authors had the followings conclusion about > it: > > "KRR is worse than neural networks, but the results are promising. An > increase of the training set size would lead to a more accurate model. But > the huge computational re- > quirements of KRR limits us to about 6% data. The train time for one KRR > model on 6% subset (about 42000 samples) is 4 hours." > > I don't know why, but I really want to see the quality of the results of > this method when using larger training sets. So my question is the > following: will such method benefit from a distributed version (mapreduce) > ? > is such thing already available ? is it interesting to the Mahout project > in > general ? I started to document about it and it seems to require some big > linear system solving. >
