Not an expert here, but the first step would be devote some time and identify which of these 112 factors are actually causative. Some domain knowledge of the data may be required. Then, you can start of with PCA.
HTH, Regards, Sivakumaran S > On 08-Aug-2016, at 3:01 PM, Tony Lane <tonylane....@gmail.com> wrote: > > Great question Rohit. I am in my early days of ML as well and it would be > great if we get some idea on this from other experts on this group. > > I know we can reduce dimensions by using PCA, but i think that does not allow > us to understand which factors from the original are we using in the end. > > - Tony L. > > On Mon, Aug 8, 2016 at 5:12 PM, Rohit Chaddha <rohitchaddha1...@gmail.com > <mailto:rohitchaddha1...@gmail.com>> wrote: > > I have a data-set where each data-point has 112 factors. > > I want to remove the factors which are not relevant, and say reduce to 20 > factors out of these 112 and then do clustering of data-points using these 20 > factors. > > How do I do these and how do I figure out which of the 20 factors are useful > for analysis. > > I see SVD and PCA implementations, but I am not sure if these give which > elements are removed and which are remaining. > > Can someone please help me understand what to do here > > thanks, > -Rohit > >
