Hi Andy,
That is the case in Spark 1.0, yes. However, as of Spark 1.1 which is
coming out very soon, you will be able to run SVD on non-TS matrices.
If you try to apply the current algorithm to a matrix with more than 10,000
columns, you will overburden the master node, which has to compute a 10k x
10k local SVD by itself.
This bottleneck has been removed in Spark 1.1, and if you really want it
now you can pull the current master from github.
Best,
Reza
On Sun, Aug 10, 2014 at 9:35 PM, Andy Zhao wrote:
> Hi guys
>
> I'm considering apply MLlib SVD in my work. But I find that in the
> document, it says: "In this release, we provide SVD computation to
> row-oriented matrices that have only a few columns, say, less than 1000,
> but
> many rows, which we call tall-and-skinny." Does that mean this SVD will
> not
> work for a matrix which have a lot of columns, say more than 1? What
> will happen if this kind of matrix is applied to SVD?
>
> Thank you ,
> Andy Zhao
>
>
>
>
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