[jira] [Commented] (SPARK-4980) Add decay factors to streaming linear methods

["Meihua Wu (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-4980?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14640708#comment-14640708 ] 

Meihua Wu commented on SPARK-4980:
----------------------------------

Hi [~freeman-lab] I am interested in implementing this. I have drafted a document to describe the algorithm and design. 

https://docs.google.com/document/d/1UfKvuaaJVQCvh-wOLLYT8l7STQFjPxE7fitZyd0tqTo/edit?usp=sharing

Thanks!

> Add decay factors to streaming linear methods
> ---------------------------------------------
>
>                 Key: SPARK-4980
>                 URL: https://issues.apache.org/jira/browse/SPARK-4980
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib, Streaming
>            Reporter: Jeremy Freeman
>            Priority: Minor
>
> Our implementation of streaming k-means uses an decay factor that allows users to control how quickly the model adjusts to new data: whether it treats all data equally, or only bases its estimate on the most recent batch. It is intuitively parameterized, and can be specified in units of either batches or points. We should add a similar decay factor to the streaming linear methods using SGD, including streaming linear regression (currently implemented) and streaming logistic regression (in development).



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