Github user mbofb commented on the pull request:
https://github.com/apache/spark/pull/4675#issuecomment-74948403
The description of RowMatrix.computeSVD and
mllib-dimensionality-reduction.html should be more precise/explicit regarding
the m x n matrix. In the current description I would conclude that n refers to
the rows. According to
http://math.stackexchange.com/questions/191711/how-many-rows-and-columns-are-in-an-m-x-n-matrix
this way of describing a matrix is only used in particular domains. I as a
reader interested on applying SVD would rather prefer the more common m x n
way of rows x columns (e.g.
http://en.wikipedia.org/wiki/Matrix_%28mathematics%29 ) which is also used in
http://en.wikipedia.org/wiki/Latent_semantic_analysis (and also within the
ARPACK manual:
â
N Integer. (INPUT) - Dimension of the eigenproblem.
NEV Integer. (INPUT) - Number of eigenvalues of OP to be computed. 0 <
NEV < N.
NCV Integer. (INPUT) - Number of columns of the matrix V (less than or
equal to N).
â
).
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