This is *exactly* the problem with LDA. You can try putting a logistic regression step in the way to combine the positive or negative values into a [0,1] value.
Or you could try LDA which is, essentially, a probabilistic version of SVD that gives you exactly what you want. On Tue, Oct 20, 2009 at 4:01 AM, prasenjit mukherjee <[email protected]>wrote: > Thanks a bunch, I fixed the problem by using Colt. > > Also I am trying to use U/V values to assign probability p(z|u) and > p(z|s). My problem is how do I interpret the -ve U/V values and assign > a +ve probability value for that entry. > > -Prasen > > On Sun, Oct 18, 2009 at 10:58 PM, Ted Dunning <[email protected]> > wrote: > > I have not worked with lingpipe, but ... > > > > When I follow the steps you are taking using R, I get this: > > > > *> docs=data.frame(d0=c(2,2,0,0), d1=c(2,2,0,0), d2=c(0,0,2,2), > > row.names=c("t0","t1","t2","t3")) > >> docs > > d0 d1 d2 > > t0 2 2 0 > > t1 2 2 0 > > t2 0 0 2 > > t3 0 0 2 > >> svd(docs) > > $d > > [1] 4.000000 2.828427 0.000000 > > > > <trimmed/> > -- Ted Dunning, CTO DeepDyve
