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| -rw-r--r-- | docs/mllib-guide.md | 7 |
1 files changed, 4 insertions, 3 deletions
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index 653848b6d4..44e6c8f58b 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -228,8 +228,8 @@ from which we recover S and V. Then we compute U via easy matrix multiplication as *U = A * V * S^-1* -Only singular vectors associated with singular values -greater or equal to MIN_SVALUE are recovered. If there are k +Only singular vectors associated with largest k singular values +are recovered. If there are k such values, then the dimensions of the return will be: * *S* is *k x k* and diagonal, holding the singular values on diagonal. @@ -237,7 +237,8 @@ such values, then the dimensions of the return will be: * *V* is *n x k* and satisfies V^TV = eye(k). All input and output is expected in sparse matrix format, 1-indexed -as tuples of the form ((i,j),value) all in RDDs. Below is example usage. +as tuples of the form ((i,j),value) all in +SparseMatrix RDDs. Below is example usage. {% highlight scala %} |