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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2015/04/28 02:59:07 UTC
[jira] [Updated] (SPARK-6323) Large rank matrix factorization with
Nonlinear loss and constraints
[ https://issues.apache.org/jira/browse/SPARK-6323?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joseph K. Bradley updated SPARK-6323:
-------------------------------------
Target Version/s: 1.5.0 (was: 1.4.0)
> Large rank matrix factorization with Nonlinear loss and constraints
> -------------------------------------------------------------------
>
> Key: SPARK-6323
> URL: https://issues.apache.org/jira/browse/SPARK-6323
> Project: Spark
> Issue Type: New Feature
> Components: ML, MLlib
> Affects Versions: 1.4.0
> Reporter: Debasish Das
> Original Estimate: 672h
> Remaining Estimate: 672h
>
> Currently ml.recommendation.ALS is optimized for gram matrix generation which scales to modest ranks. The problems that we can solve are in the normal equation/quadratic form: 0.5x'Hx + c'x + g(z)
> g(z) can be one of the constraints from Breeze proximal library:
> https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/optimize/proximal/Proximal.scala
> In this PR we will re-use ml.recommendation.ALS design and come up with ml.recommendation.ALM (Alternating Minimization). Thanks to [~mengxr] recent changes, it's straightforward to do it now !
> ALM will be capable of solving the following problems: min f ( x ) + g ( z )
> 1. Loss function f ( x ) can be LeastSquareLoss and LoglikelihoodLoss. Most likely we will re-use the Gradient interfaces already defined and implement LoglikelihoodLoss
> 2. Constraints g ( z ) supported are same as above except that we don't support affine + bounds yet Aeq x = beq , lb <= x <= ub yet. Most likely we don't need that for ML applications
> 3. For solver we will use breeze.optimize.proximal.NonlinearMinimizer which in turn uses projection based solver (SPG) or proximal solvers (ADMM) based on convergence speed.
> https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/optimize/proximal/NonlinearMinimizer.scala
> 4. The factors will be SparseVector so that we keep shuffle size in check. For example we will run with 10K ranks but we will force factors to be 100-sparse.
> This is closely related to Sparse LDA https://issues.apache.org/jira/browse/SPARK-5564 with the difference that we are not using graph representation here.
> As we do scaling experiments, we will understand which flow is more suited as ratings get denser (my understanding is that since we already scaled ALS to 2 billion ratings and we will keep sparsity in check, the same 2 billion flow will scale to 10K ranks as well)...
> This JIRA is intended to extend the capabilities of ml recommendation to generalized loss function.
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