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https://issues.apache.org/jira/browse/SPARK-14533?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15235008#comment-15235008
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Apache Spark commented on SPARK-14533:
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User 'srowen' has created a pull request for this issue:
https://github.com/apache/spark/pull/12299
> RowMatrix.computeCovariance inaccurate when values are very large
> -----------------------------------------------------------------
>
> Key: SPARK-14533
> URL: https://issues.apache.org/jira/browse/SPARK-14533
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.6.1, 2.0.0
> Reporter: Sean Owen
> Assignee: Sean Owen
> Priority: Minor
>
> The following code will produce a Pearson correlation that's quite different
> from 0, sometimes outside [-1,1] or even NaN:
> {code}
> val a = RandomRDDs.normalRDD(sc, 100000, 10).map(_ + 1000000000.0)
> val b = RandomRDDs.normalRDD(sc, 100000, 10).map(_ + 1000000000.0)
> val p = Statistics.corr(a, b, method = "pearson")
> {code}
> This is a "known issue" to some degree, given how Cov(X,Y) is calculated in
> {{RowMatrix.getCovariance}}, as Cov(X,Y) = E[XY] - E[X]E[Y]. The easier and
> more accurate approach involves just centering the input before computing the
> Gramian, but this would be inefficient for sparse data.
> However, for dense data -- which includes the code paths that compute
> correlations -- this approach is quite sensible. This would improve accuracy
> for the dense row case, at least.
> Also, the mean column values computed in this method can be computed more
> simply and accurately from {{computeColumnSummaryStatistics()}}
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