Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/2070#discussion_r16523887
--- Diff: docs/mllib-dimensionality-reduction.md ---
@@ -119,14 +137,13 @@ statistical method to find a rotation such that the
first coordinate has the lar
possible, and each succeeding coordinate in turn has the largest variance
possible. The columns of
the rotation matrix are called principal components. PCA is used widely in
dimensionality reduction.
-MLlib supports PCA for tall-and-skinny matrices stored in row-oriented
format.
+MLlib supports PCA for matrices stored in row-oriented format.
--- End diff --
I think we still need tall-and-skinny matrices for PCA.
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