[jira] [Commented] (SPARK-4981) Add a streaming singular value decomposition

["Reza Zadeh (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-4981?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14259789#comment-14259789 ] 

Reza Zadeh commented on SPARK-4981:
-----------------------------------

We could do matrix completion (least squares objective, reqularized, note that this is not SVD) in a streaming fashion using Stochastic Gradient Descent.

See the update equations in Algorithm 1:
http://stanford.edu/~rezab/papers/factorbird.pdf

The stream is over individual entries (as opposed a whole row/column).

We should probably do streaming matrix completion before streaming SVD.

> Add a streaming singular value decomposition
> --------------------------------------------
>
>                 Key: SPARK-4981
>                 URL: https://issues.apache.org/jira/browse/SPARK-4981
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib, Streaming
>            Reporter: Jeremy Freeman
>
> This is for tracking WIP on a streaming singular value decomposition implementation. This will likely be more complex than the existing streaming algorithms (k-means, regression), but should be possible using the family of sequential update rule outlined in this paper:
> "Fast low-rank modifications of the thin singular value decomposition"
> by Matthew Brand
> http://www.stat.osu.edu/~dmsl/thinSVDtracking.pdf



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