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[MLlib] Update SVD documentation in IndexedRowMatrix #2389

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[MLlib] Update SVD documentation in IndexedRowMatrix #2389

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715da25
Updated computeSVD documentation IndexedRowMatrix
rezazadeh Sep 14, 2014
7fa1313
Update svd docs
rezazadeh Sep 14, 2014
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12 changes: 4 additions & 8 deletions mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
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Expand Up @@ -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:
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