I honestly don't know. Haven't really researched them much. :) There's a caption for a figure on Wikipedia that says that "After many iterations the grid tends to approximate the data distribution" [1]. (yes, I know Wikipedia...)
[1] http://en.wikipedia.org/wiki/Self-organizing_map On Sat, Mar 30, 2013 at 2:21 PM, Sean Owen <sro...@gmail.com> wrote: > 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 >
