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https://issues.apache.org/jira/browse/SPARK-3012?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15745388#comment-15745388
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Dhaval Modi commented on SPARK-3012:
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I have implemented Mahalanobis Distance in Spark 1.6.2+ using Breeze 0.12
libraries. One can refer below scala code at:
https://github.com/dhmodi/MahalanobisDistance
> Standardized Distance Functions between two Vectors for MLlib
> -------------------------------------------------------------
>
> Key: SPARK-3012
> URL: https://issues.apache.org/jira/browse/SPARK-3012
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Yu Ishikawa
> Priority: Minor
>
> Most of the clustering algorithms need distance functions between two Vectors.
> We should include the standardized distance function library in MLlib.
> I think that the standardized distance functions help us to implement more
> machine learning algorithms efficiently.
> h3. For example
> - Chebyshev Distance
> - Cosine Distance
> - Euclidean Distance
> - Mahalanobis Distance
> - Manhattan Distance
> - Minkowski Distance
> - SquaredEuclidean Distance
> - Tanimoto Distance
> - Weighted Distance
> - WeightedEuclidean Distance
> - WeightedManhattan Distance
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