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https://issues.apache.org/jira/browse/SPARK-6867?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-6867:
-----------------------------------
Assignee: Apache Spark
> Dropout regularization
> ----------------------
>
> Key: SPARK-6867
> URL: https://issues.apache.org/jira/browse/SPARK-6867
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Rakesh Chalasani
> Assignee: Apache Spark
> Priority: Minor
>
> Linear models is MLLIB so far support no regularization, L1 and L2. Another
> more recently popularized method for regularization is dropout
> [http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf]. The dropout
> regularization basically randomly omit some of the input features at each
> iteration.
> Though this approach is particularly used in training deep networks, they
> could also be very useful on a linear models as if promotes adaptive
> regularization. This approach is particularly useful in NLP
> [http://papers.nips.cc/paper/4882-dropout-training-as-adaptive-regularization.pdf]
> and, because of its simplicity can be easily adopted for streaming linear
> models as well.
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