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https://issues.apache.org/jira/browse/SPARK-10356?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14721502#comment-14721502
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Carsten Schnober commented on SPARK-10356:
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According to
[[Wikipedia][https://en.wikipedia.org/wiki/Norm_%28mathematics%29#p-norm], each
value's absolute value should be used to compute the norm:
{||x||_p := (sum(|x|^p)^1/p}
For p = 1, this results in:
{||x||_1 := sum(|x|)}
I suppose the issue is thus actually located in the {norm()} method.
> MLlib: Normalization should use absolute values
> -----------------------------------------------
>
> Key: SPARK-10356
> URL: https://issues.apache.org/jira/browse/SPARK-10356
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.4.1
> Reporter: Carsten Schnober
> Labels: easyfix
> Original Estimate: 2h
> Remaining Estimate: 2h
>
> The normalizer does not handle vectors with negative values properly. It can
> be tested with the following code
> {code}
> val normalized = new Normalizer(1.0).transform(v: Vector)
> normalizer.toArray.sum == 1.0
> {code}
> This yields true if all values in Vector v are positive, but false when v
> contains one or more negative values. This is because the values in v are
> taken immediately without applying {{abs()}},
> This (probably) does not occur for {{p=2.0}} because the values are squared
> and hence positive anyway.
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