Are SOMs actually good at dimension reduction? I had understood it to just be a visualization technique. You end up with a mapping with the property that things that are near are similar, but no guarantee that things that are similar are near.
On Sat, Mar 30, 2013 at 12:06 PM, Dan Filimon <dangeorge.fili...@gmail.com> wrote: > Hi, > > I have a larger assignment to work on for my Machine Learning course this > semester and I can pick one of 4 problems to solve. > > One of them, is implementing self organizing maps and using them to cluster > the Localization Data for Person Activity Data Set [1] and evaluate the > clustering with the Dunn Index and F-measure. > > I vaguely recall talking to Ted about self organizing maps as a way of > achieving dimensionality reduction, so that's where it could be useful. > > I need to pick a problem anyway and was wondering if there's any sort of > interest in this one. > If yes, I could work on an implementation for Mahout (likely non MapReduce, > at least for the purposes of this assignment). > > Thoughts? > > [1] > http://archive.ics.uci.edu/ml/datasets/Localization+Data+for+Person+Activity
