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From "Debasish Das (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (SPARK-2426) Quadratic Minimization for MLlib ALS
Date Tue, 24 Mar 2015 06:11:53 GMT

    [ https://issues.apache.org/jira/browse/SPARK-2426?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14377357#comment-14377357
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Debasish Das edited comment on SPARK-2426 at 3/24/15 6:11 AM:
--------------------------------------------------------------

[~acopich] From your comment before "Anyway, l2 regularized stochastic matrix decomposition
problem is defined as follows
Minimize w.r.t. W and H : ||R - W*H|| + \lambda(||W|| + ||H||)
under non-negativeness and normalization constraints.
.", could you please point me to a good reference with application to collaborative filtering/topic
modeling ? Stochastic matrix decomposition is what we can do in this PR now https://github.com/apache/spark/pull/3221....

For MAP loss, I will open up a PR in a week through JIRA https://issues.apache.org/jira/browse/SPARK-6323...I
am very curious how much slower we get compared to stochastic matrix decomposition using ALS


was (Author: debasish83):
[~acopich] From your comment before "Anyway, l2 regularized stochastic matrix decomposition
problem is defined as follows
Minimize w.r.t. W and H : ||R - W*H|| + \lambda(||W|| + ||H||)
under non-negativeness and normalization constraints.
|| . || stands for Frobenius norm (or l1).", could you please point me to a good reference
with application to collaborative filtering/topic modeling ? Stochastic matrix decomposition
is what we can do in this PR now https://github.com/apache/spark/pull/3221....

For MAP loss, I will open up a PR in a week through JIRA https://issues.apache.org/jira/browse/SPARK-6323...I
am very curious how much slower we get compared to stochastic matrix decomposition using ALS

> Quadratic Minimization for MLlib ALS
> ------------------------------------
>
>                 Key: SPARK-2426
>                 URL: https://issues.apache.org/jira/browse/SPARK-2426
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>    Affects Versions: 1.3.0
>            Reporter: Debasish Das
>            Assignee: Debasish Das
>   Original Estimate: 504h
>  Remaining Estimate: 504h
>
> Current ALS supports least squares and nonnegative least squares.
> I presented ADMM and IPM based Quadratic Minimization solvers to be used for the following
ALS problems:
> 1. ALS with bounds
> 2. ALS with L1 regularization
> 3. ALS with Equality constraint and bounds
> Initial runtime comparisons are presented at Spark Summit. 
> http://spark-summit.org/2014/talk/quadratic-programing-solver-for-non-negative-matrix-factorization-with-spark
> Based on Xiangrui's feedback I am currently comparing the ADMM based Quadratic Minimization
solvers with IPM based QpSolvers and the default ALS/NNLS. I will keep updating the runtime
comparison results.
> For integration the detailed plan is as follows:
> 1. Add QuadraticMinimizer and Proximal algorithms in mllib.optimization
> 2. Integrate QuadraticMinimizer in mllib ALS



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