On Tue, Oct 20, 2009 at 10:22 PM, Ted Dunning <[email protected]> wrote: > 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.
Thanks for the pointer, Also can you explain ( or refer an article ) it a little bit on how to use log regression to get a [0,1] value out of U/V vectors. > > Or you could try LDA which is, essentially, a probabilistic version of SVD > that gives you exactly what you want. That was my first attempt. But the data is very sensitive to overfitting/underfitting. And since I dont even know the approximate L ( no. of latent vars ) it is becoming difficult for me to use LDA/PLSI/approximate-SVD. -Prasen > > 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 >
