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https://issues.apache.org/jira/browse/SPARK-4039?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15146465#comment-15146465
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yuhao yang commented on SPARK-4039:
-----------------------------------
https://github.com/hhbyyh/spark/blob/kmeansSparse/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
I got an implementation there that supports sparse k-means centers. The
calculation pattern can be switched via an extra parameter and users can choose
which pattern to use. As expected, it can save a lot of memory according to the
average sparsity of the cluster centers, but will consume much more time also.
For feature dimension of 10M and nonzero rate 1e-6, it can reduce memory
consumption by 40 times yet used 700% time. Welcome to use if you really need
to support large dimension k-means.
> KMeans support sparse cluster centers
> -------------------------------------
>
> Key: SPARK-4039
> URL: https://issues.apache.org/jira/browse/SPARK-4039
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.1.0
> Reporter: Antoine Amend
> Labels: clustering
>
> When the number of features is not known, it might be quite helpful to create
> sparse vectors using HashingTF.transform. KMeans transforms centers vectors
> to dense vectors
> (https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L307),
> therefore leading to OutOfMemory (even with small k).
> Any way to keep vectors sparse ?
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