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From "Xiangrui Meng (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (SPARK-6323) Large rank matrix factorization with Nonlinear loss and constraints
Date Fri, 13 Mar 2015 17:13:40 GMT

[ https://issues.apache.org/jira/browse/SPARK-6323?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14360679#comment-14360679
]

Xiangrui Meng edited comment on SPARK-6323 at 3/13/15 5:13 PM:
---------------------------------------------------------------

> The problems that we can solve are in the normal equation/quadratic form: 0.5x'Hx + c'x
+ g(z)

You call g(z) "constraints" but it looks like the regularization term in the objective function,
and you didn't mention what z is.

> ALM will be capable of solving the following problems: min f ( x ) + g (z)

Are they sub-problems of the matrix factorization? If yes, could you also tell the global
objective? For example, in ALS, the global objective is

{code}
minimize \frac{1}[2} \sum_{ij \in \Omega} (r_{ij} - u_i^T v_j)^2
{code}

and if we take alternating directions, the problem in each step is decoupled into many sub-problems
(least squares).

{code}
minimize \frac{1}{2} \sum_{j, ij \in \Omega} (r_{ij}  - u_i^T v_j)^2  (sub-problem for u_i)
{code}

We can add the nonnegative constraints to the global objective, and then the sub-problems
receive the same constraints. I can see other loss may work, but I cannot clearly see the
benefits of using other losses, which usually make the problem much harder to solve. Any papers
for reference?

Another issue is dealing with very frequent items (https://issues.apache.org/jira/browse/SPARK-3735).
We plan to assemble and send partial AtA directly. But this only works if the subproblems
can be expressed using normal equation. I think it only applies to squared loss.

> 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)...

Any papers using rank ~10K?

was (Author: mengxr):

> The problems that we can solve are in the normal equation/quadratic form: 0.5x'Hx + c'x
+ g(z)

You call g(z) "constraints" but it looks like the regularization term in the objective function,
and you didn't mention what z is.

> ALM will be capable of solving the following problems: min f (x) + g (z)

Are they sub-problems of the matrix factorization? If yes, could you also tell the global
objective? For example, in ALS, the global objective is

minimize \frac{1}[2} \sum_{ij \in \Omega} (r_{ij} - u_i^T v_j)^2

and if we take alternating directions, the problem in each step is decoupled into many sub-problems
(least squares).

minimize \frac{1}{2} \sum_{j, ij \in \Omega} (r_{ij}  - u_i^T v_j)^2  (sub-problem for u_i)

We can add the nonnegative constraints to the global objective, and then the sub-problems
receive the same constraints. I can see other loss may work, but I cannot clearly see the
benefits of using other losses, which usually make the problem much harder to solve. Any papers
for reference?

Another issue is dealing with very frequent items (https://issues.apache.org/jira/browse/SPARK-3735).
We plan to assemble and send partial AtA directly. But this only works if the subproblems
can be expressed using normal equation. I think it only applies to squared loss.

> 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)...

Any papers using rank ~10K?

> 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
>             Fix For: 1.4.0
>
>   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, LoglikelihoodLoss and HingeLoss. 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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