ok thank you. That's what I thought. Thank you for confirming.
On Fri, Dec 23, 2011 at 10:51 AM, Ted Dunning <[email protected]> wrote: > All of the math is done initially using normal distributions, but I doubt > that there is any detectable difference in practice between normal and > uniform. To be precise, the only time I would expect to be able to see a > difference between uniform and normal is in cases that have less than a > dozen non-zeros per row on average in A. That is pretty pathological. > > Having a non-zero mean could have bad effects if there were a huge number > of non-zero elements, but probably has little practical impact. > > The ternary distribution is just a flop-saver as you suggest. A sparse > ternary distribution worries me a bit, but the convergence guarantees are > pretty compelling. A very sparse ternary distribution is probably very bad > for the data we focus on because you have a significant chance of not using > any element from some rows of A. > > On Fri, Dec 23, 2011 at 10:39 AM, Dmitriy Lyubimov <[email protected]>wrote: > >> 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. >>
