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author | Reza Zadeh <rizlar@gmail.com> | 2014-01-17 13:39:40 -0800 |
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committer | Reza Zadeh <rizlar@gmail.com> | 2014-01-17 13:39:40 -0800 |
commit | d28bf4182758f08862d5838c918756801a9d7327 (patch) | |
tree | c58296f3fc31ebcfe7bcf8bdd6ca786b0e8cd400 /docs | |
parent | 845e568fada0550e632e7381748c5a9ebbe53e16 (diff) | |
download | spark-d28bf4182758f08862d5838c918756801a9d7327.tar.gz spark-d28bf4182758f08862d5838c918756801a9d7327.tar.bz2 spark-d28bf4182758f08862d5838c918756801a9d7327.zip |
changes from PR
Diffstat (limited to 'docs')
-rw-r--r-- | docs/mllib-guide.md | 5 |
1 files changed, 3 insertions, 2 deletions
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index a140ecb618..26350ce106 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -445,11 +445,12 @@ Given an *m x n* matrix *A*, we can compute matrices *U, S, V* such that *A = U * S * V^T* -There is no restriction on m, but we require n^2 doubles to fit in memory. +There is no restriction on m, but we require n^2 doubles to +fit in memory locally on one machine. Further, n should be less than m. The decomposition is computed by first computing *A^TA = V S^2 V^T*, -computing svd locally on that (since n x n is small), +computing SVD locally on that (since n x n is small), from which we recover S and V. Then we compute U via easy matrix multiplication as *U = A * V * S^-1* |