or, rather, that they are of the same length. although any random distribution actually should eventually converge on the same length for sufficiently high dimensional vectors.
also normal distribution would tend to keep vectors closer to subspaces spanned by axes , thus probably ensuring better orthogonality guarantee.. On Fri, Dec 23, 2011 at 10:33 AM, Dmitriy Lyubimov <[email protected]> wrote: > Ted, > > is Ternary matrix somehow better than uniform or normal for random > projection? or it is just flops saving technique? > > Original study actually implied unit gaussian vectors, which i think > is not quite exactly what we are doing with either technique. I think > it is important that vectors are unitary, that way it better figures > the subspace with major variances i think.
