Antoine Amend created SPARK-4039:
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Summary: KMeans support HashingTF vectors
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
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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