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https://issues.apache.org/jira/browse/SPARK-2426?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14375325#comment-14375325
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Debasish Das commented on SPARK-2426:
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[~acopich] "There's a completely different loss... BTW, we've used a
factorisation with the loss you've described as an initial approximation for
PLSA. It gave a significant speed-up." Could you help adding some testcases and
driver for the PLSA approximation ? the PR
https://github.com/apache/spark/pull/3221 has now the LSA constraints and least
square loss...
Idea here is to do probability simplex on user side, bounds on the item side
and normalization on item columns at each ALS iteration...The MAP loss is
tracked through https://issues.apache.org/jira/browse/SPARK-6323 but the solve
idea will be very similar as I mentioned before and so we can re-use the flow
test-cases...We can discuss more on the PR...It will be great if you can help
add examples.mllib.PLSA as well that will driver both PLSA through ALS and ALM
(alternating MAP loss optimization)...
> 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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