diff options
Diffstat (limited to 'mllib/src')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala | 12 |
1 files changed, 4 insertions, 8 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala index ac6eaea3f4..5c1acca0ec 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala @@ -76,16 +76,12 @@ class IndexedRowMatrix( } /** - * Computes the singular value decomposition of this matrix. + * Computes the singular value decomposition of this IndexedRowMatrix. * Denote this matrix by A (m x n), this will compute matrices U, S, V such that A = U * S * V'. * - * There is no restriction on m, but we require `n^2` doubles to fit in memory. - * Further, n should be less than m. - - * The decomposition is computed by first computing A'A = V S^2 V', - * 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). - * Note that this approach requires `O(n^3)` time on the master node. + * The cost and implementation of this method is identical to that in + * [[org.apache.spark.mllib.linalg.distributed.RowMatrix]] + * With the addition of indices. * * At most k largest non-zero singular values and associated vectors are returned. * If there are k such values, then the dimensions of the return will be: |