It might make a difference that you're just running 1 iteration. Normally it's run to 'convergence' -- or here let's say, 10+ iterations to be safe.
This is the QR factorization of Y' * Y at the finish? This seems like it can't be right... Y has only 5 vectors in 10 dimensions and Y' * Y is certainly not invertible. I get: 1.20857 -0.20462 0.08707 -0.16972 0.17038 0.00342 0.24459 -0.23287 0.51142 -0.06083 0.00000 1.13242 0.23155 0.24354 0.32995 0.47781 -0.02832 0.43071 -0.24968 0.41470 0.00000 0.00000 0.91070 0.37732 0.05296 0.39886 -0.62426 0.07809 0.53891 0.24877 0.00000 0.00000 0.00000 0.69369 -0.21648 -0.10501 0.09706 -0.03683 -0.10512 0.02849 0.00000 0.00000 0.00000 0.00000 0.60165 0.37106 -0.00193 -0.23392 0.10109 -0.09897 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 -0.00000 -0.00000 -0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 -0.00000 -0.00000 -0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 -0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 -0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 I think there are some other differences here but probably not meaningful in this context. For example I was doing implicit-feedback ALS. (But the result above is from an Octave implementation of "regular" ALS like what your'e running) There are a bunch of useful thoughts here I am going to both read up and explore as conditions. On Thu, Apr 4, 2013 at 8:54 PM, Koobas <[email protected]> wrote: > BTW, my initialization of X and Y is simply random: > X = rand(m,k); > Y = rand(k,n); > > > > On Thu, Apr 4, 2013 at 3:51 PM, Koobas <[email protected]> wrote: > >> It's done in one iteration. >> This is the R from QR factorization: >> >> 5.0663 5.8122 4.9704 4.3987 6.3400 4.5970 5.0334 >> 4.2581 3.3808 5.3250 >> 0 2.4036 1.1722 2.3296 1.6580 0.4575 1.1706 >> 2.1040 1.6738 1.4839 >> 0 0 1.5085 0.0966 1.2581 0.5236 0.4712 >> -0.0411 0.3143 0.5957 >> 0 0 0 1.8682 0.1834 -0.3244 -0.0073 >> 0.3817 1.1673 0.4783 >> 0 0 0 0 1.9569 0.8666 0.3201 >> -0.4167 0.0732 0.3114 >> 0 0 0 0 0 1.3520 0.2326 >> -0.1156 -0.2793 0.0103 >> 0 0 0 0 0 0 1.1689 >> 0.3151 0.0590 0.0435 >> 0 0 0 0 0 0 0 >> 1.6296 -0.3494 -0.0024 >> 0 0 0 0 0 0 >> 0 0 1.4307 0.1803 >> 0 0 0 0 0 0 >> 0 0 0 1.1404 >> >> >> >>
