Choosing lambda = 0.1 shouldn't lead to the error you got. This is probably a bug. Do you mind sharing a small amount of data that can re-produce the error? -Xiangrui
On Fri, Mar 7, 2014 at 8:24 AM, Debasish Das <debasish.da...@gmail.com> wrote: > Hi Xiangrui, > > I used lambda = 0.1...It is possible that 2 users ranked in movies in a > very similar way... > > I agree that increasing lambda will solve the problem but you agree this is > not a solution...lambda should be tuned based on sparsity / other criteria > and not to make a linearly dependent hessian matrix linearly > independent... > > Thanks. > Deb > > > > > > On Thu, Mar 6, 2014 at 7:20 PM, Xiangrui Meng <men...@gmail.com> wrote: > >> If the matrix is very ill-conditioned, then A^T A becomes numerically >> rank deficient. However, if you use a reasonably large positive >> regularization constant (lambda), "A^T A + lambda I" should be still >> positive definite. What was the regularization constant (lambda) you >> set? Could you test whether the error still happens when you use a >> large lambda? >> >> Best, >> Xiangrui >>
