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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1737) Add statistical whitening transformation to machine learning library
Date Sun, 27 Sep 2015 17:47:04 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1737?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14909811#comment-14909811
] 

ASF GitHub Bot commented on FLINK-1737:
---------------------------------------

Github user daniel-pape commented on a diff in the pull request:

    https://github.com/apache/flink/pull/1078#discussion_r40507046
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/math/DenseVector.scala
---
    @@ -102,6 +102,38 @@ case class DenseVector(
         }
       }
     
    +  /** Returns the outer product (a.k.a. Kronecker product) of `this`
    +    * with `other`. The result will given in [[org.apache.flink.ml.math.SparseMatrix]]
    +    * representation if `other` is sparse and as [[org.apache.flink.ml.math.DenseMatrix]]
otherwise.
    +    *
    +    * @param other a Vector
    +    * @return the [[org.apache.flink.ml.math.Matrix]] which equals the outer product
of `this`
    +    *         with `other.`
    +    */
    +  override def outer(other: Vector): Matrix = {
    +    val numRows = size
    +    val numCols = other.size
    +
    +    other match {
    +      case SparseVector(size, indices, data_) =>
    +        val entries: Array[(Int, Int, Double)] = for {
    --- End diff --
    
    Should be fixed by now.


> Add statistical whitening transformation to machine learning library
> --------------------------------------------------------------------
>
>                 Key: FLINK-1737
>                 URL: https://issues.apache.org/jira/browse/FLINK-1737
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Till Rohrmann
>            Assignee: Daniel Pape
>              Labels: ML, Starter
>
> The statistical whitening transformation [1] is a preprocessing step for different ML
algorithms. It decorrelates the individual dimensions and sets its variance to 1.
> Statistical whitening should be implemented as a {{Transfomer}}.
> Resources:
> [1] [http://en.wikipedia.org/wiki/Whitening_transformation]



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