Note that since precise non-negative factorization is generally not possible and this would constrain the factorization even further, it will not generally be possible to factorize a matrix like this. Then again, since the W = H' constraint seems to make the problem convex, it may actually make the optimization problem of finding non-negative H that minimizes the norm of the error easier.
On Wed, May 6, 2015 at 2:54 PM, Tim Holy <[email protected]> wrote: > What you're asking might be called "nonnegative Cholesky factorization." > From > a brief search, there seems to be a dearth of literature on that subject. > You > could be the first! > > --Tim > > On Wednesday, May 06, 2015 09:59:37 AM Lytu wrote: > > NMF.jl package can factorize a matrix 3x3 and give two matrix (a 3x2 > matrix > > H and a 2x3 matrix W), W is the transpose of a matrix H? > > A = H * W > > E.g: > > import NMF > > A=[5.0 3.0 6.0;3.0 9.0 12.0;6.0 12.0 17.0] > > H, W= NMF.randinit(A, 2) > > > > this code give me 2 matrix H and W > > > > I know that NMF.randinit() gives two matrix H(3x2) and W(2x3) but W is > not > > the transpose of a matrix H. > > > > I would like to know there is a function in NMF package that can give > this > > such result: > > A = H * W with W=H' > > Thank you > >
