I got all three U, V & sigma from ssvd, however which to use as input to
canopy?
On May 24, 2013 6:47 AM, "Dmitriy Lyubimov" <dlie...@gmail.com> wrote:
> I think you want U*Sigma
>
> What you want is ssvd ... -pca true ... -us true ... see the manual
>
>
>
>
> On Thu, May 23, 2013 at 6:07 PM, Rajesh Nikam <rajeshni...@gmail.com>
> wrote:
>
> > Sorry for confusion. Here number of clusters are decided by canopy. With
> > data as it has 60 to 70 clusters.
> >
> > My question is which part from ssvd output U, V, Sigma should be used as
> > input to canopy?
> > On May 24, 2013 3:56 AM, "Ted Dunning" <ted.dunn...@gmail.com> wrote:
> >
> > > Rajesh,
> > >
> > > This is very confusing.
> > >
> > > You have 1500 things that you are clustering into more than 1400
> > clusters.
> > >
> > > There is no way for most of these clusters to have >1 member just
> because
> > > there aren't enough clusters compared to the items.
> > >
> > > Is there a typo here?
> > >
> > >
> > >
> > >
> > > On Thu, May 23, 2013 at 5:34 AM, Rajesh Nikam <rajeshni...@gmail.com>
> > > wrote:
> > >
> > > > Hi,
> > > >
> > > > I have input test set of 1500 instances with 1000+ features. I want
> to
> > to
> > > > SVD to reduce features. I have followed following steps with generate
> > > 1400+
> > > > clusters 99% of clusters contain 1 instance :(
> > > >
> > > > Please let me know what is wrong in below steps -
> > > >
> > > >
> > > > mahout arff.vector --input /mnt/cluster/t/input-set.arff --output
> > > > /user/hadoop/t/input-set-vector/ --dictOut
> > /mnt/cluster/t/input-set-dict
> > > >
> > > > mahout ssvd --input /user/hadoop/t/input-set-vector/ --output
> > > > /user/hadoop/t/input-set-svd/ -k 200 --reduceTasks 2 -ow
> > > >
> > > > mahout canopy -i */user/hadoop/t/input-set-svd/U* -o
> > > > /user/hadoop/t/input-set-canopy-centroids -dm
> > > > org.apache.mahout.common.distance.TanimotoDistanceMeasure *-t1 0.001
> > -t2
> > > > 0.002*
> > > >
> > > > mahout kmeans -i */user/hadoop/t/input-set-svd/U* -c
> > > > /user/hadoop/t/input-set-canopy-centroids/clusters-0-final -cl -o
> > > > /user/hadoop/t/input-set-kmeans-clusters -ow -x 10 -dm
> > > > org.apache.mahout.common.distance.TanimotoDistanceMeasure
> > > >
> > > > mahout clusterdump -dt sequencefile -i
> > > > /user/hadoop/t/input-set-kmeans-clusters/clusters-1-final/ -n 20 -b
> 100
> > > -o
> > > > /mnt/cluster/t/cdump-input-set.txt -p
> > > > /user/hadoop/t/input-set-kmeans-clusters/clusteredPoints/ --evaluate
> > > >
> > > > Thanks in advance !
> > > >
> > > > Rajesh
> > > >
> > > >
> > > >
> > > >
> > > > On Wed, May 22, 2013 at 2:18 AM, Dmitriy Lyubimov <dlie...@gmail.com
> >
> > > > wrote:
> > > >
> > > > > PPS As far as the tool for arff, i am frankly not sure. but it
> sounds
> > > > like
> > > > > you've already solved this.
> > > > >
> > > > >
> > > > > On Tue, May 21, 2013 at 1:41 PM, Dmitriy Lyubimov <
> dlie...@gmail.com
> > >
> > > > > wrote:
> > > > >
> > > > > > ps as far as U, V data "close to zero", yes that's what you'd
> > expect.
> > > > > >
> > > > > > Here, by "close to zero" it still means much bigger than a
> rounding
> > > > error
> > > > > > of course. e.g. 1E-12 is indeed a small number, and 1E-16 to
> 1E-18
> > > > would
> > > > > be
> > > > > > indeed "close to zero" for the purposes of singularity.
> 1E-2..1E-5
> > > are
> > > > > > actually quite "sizeable" numbers by the scale of IEEE 754
> > > > arithmetics.
> > > > > >
> > > > > > U and V are orthonormal (which means their column vectors have
> > > > euclidiean
> > > > > > norm of 1) . Note that for large m and n (large inputs) they are
> > also
> > > > > > extremely skinny. The larger input is, the smaller the element
> of U
> > > > > or/and
> > > > > > V is gonna be.
> > > > > >
> > > > > >
> > > > > >
> > > > > > On Tue, May 21, 2013 at 8:48 AM, Dmitriy Lyubimov <
> > dlie...@gmail.com
> > > > > >wrote:
> > > > > >
> > > > > >> Sounds like dimensionality reduction to me. You may want to use
> > ssvd
> > > > > -pca
> > > > > >>
> > > > > >> Apologies for brevity. Sent from my Android phone.
> > > > > >> -Dmitriy
> > > > > >> On May 21, 2013 6:27 AM, "Rajesh Nikam" <rajeshni...@gmail.com>
> > > > wrote:
> > > > > >>
> > > > > >>> Hello Ted,
> > > > > >>>
> > > > > >>> Thanks for reply.
> > > > > >>>
> > > > > >>> I have started exploring SVD based on its mention of could help
> > to
> > > > drop
> > > > > >>> features which are not relevant for clustering.
> > > > > >>>
> > > > > >>> My objective is reduce number of features before passing them
> to
> > > > > >>> clustering
> > > > > >>> and just keep important features.
> > > > > >>>
> > > > > >>> arff/csv==> ssvd (for dimensionality reduction) ==> clustering
> > > > > >>>
> > > > > >>> Could you please illustrate mahout props to join above
> pipeline.
> > > > > >>>
> > > > > >>> I think, Lanczos SVD needs to be used for mxm matrix.
> > > > > >>>
> > > > > >>> I have tried check ssvd, I have used arff.vector to covert
> > arff/csv
> > > > to
> > > > > >>> vector file which is then give as input to ssvd and them dumped
> > U,
> > > V
> > > > > and
> > > > > >>> sigma using vectordump.
> > > > > >>>
> > > > > >>> I see most of the values dumped are near to 0. I dont
> understand
> > is
> > > > > this
> > > > > >>> correct or not.
> > > > > >>>
> > > > > >>>
> > > > > >>>
> > > > >
> > > >
> > >
> >
> {0:0.01066724825049657,1:0.016715498597386844,2:2.0187750952311708E-4,3:3.401020567221039E-4,4:-1.2388403347280688E-4,5:6.41502463540719E-5,6:-1.359187582538833E-4,7:6.329813140445419E-5,8:1.670015585746444E-4,9:3.5415113034592744E-4,10:7.108868213280763E-4,11:0.020553517552052456,12:-0.015118680942548916,13:0.007981746711271956,14:-0.003251236468768259,15:0.0038075014396303053,16:-0.0010925318534013683,17:-0.0026943024876179833,18:-0.001744794617721648,19:-0.0024528466548735714}
> > > > > >>>
> > > > > >>>
> > > > >
> > > >
> > >
> >
> {0:0.029978614322360833,1:-0.01431521245087889,2:1.3318592088199427E-4,3:1.495356283071516E-4,4:8.762709213918985E-5,5:1.2765191352425177E-
> > > > > >>>
> > > > > >>> Thanks,
> > > > > >>> Rajesh
> > > > > >>>
> > > > > >>>
> > > > > >>>
> > > > > >>> On Tue, May 21, 2013 at 11:35 AM, Ted Dunning <
> > > ted.dunn...@gmail.com
> > > > >
> > > > > >>> wrote:
> > > > > >>>
> > > > > >>> > Are you using Lanczos instead of SSVD for a reason?
> > > > > >>> >
> > > > > >>> >
> > > > > >>> >
> > > > > >>> >
> > > > > >>> > On Mon, May 20, 2013 at 4:13 AM, Rajesh Nikam <
> > > > rajeshni...@gmail.com
> > > > > >
> > > > > >>> > wrote:
> > > > > >>> >
> > > > > >>> > > Hello,
> > > > > >>> > >
> > > > > >>> > > I have arff / csv file containing input data that I want to
> > > pass
> > > > to
> > > > > >>> svd :
> > > > > >>> > > Lanczos Singular Value Decomposition.
> > > > > >>> > >
> > > > > >>> > > Which tool to use to convert it to required format ?
> > > > > >>> > >
> > > > > >>> > > Thanks in Advance !
> > > > > >>> > >
> > > > > >>> > > Thanks,
> > > > > >>> > > Rajesh
> > > > > >>> > >
> > > > > >>> >
> > > > > >>>
> > > > > >>
> > > > > >
> > > > >
> > > >
> > >
> >
>
