[petsc-dev] (no subject)

[Richard Tran Mills]

Thanks for sharing this, Barry. I haven't had time to read their paper, but
it looks worth a read.

Hong, since many machine-learning or data-mining problems can be cast as
linear algebra problems (several examples involving eigenproblems come to
mind), I'm guessing that there must be several people using PETSc (with
SLEPc, likely) in this this area, but I don't think I've come across any
published examples. What have others seen?

Most of the machine learning and data-mining papers I read seem employ
sequential algorithms or, at most, algorithms targeted at on-node
parallelism only. With available data sets getting as large and easily
available as they are, I'm surprised that there isn't more focus on doing
things with distributed parallelism. One of my cited papers is on a
distributed parallel k-means implementation I worked on some years ago: we
didn't do anything especially clever with it, but today it is still one of
the *only* parallel clustering publications I've seen.

I'd love to 1) hear about what other machine-learning or data-mining
applications using PETSc that others have come across and 2) hear about
applications in this area where people aren't using PETSc but it looks like
they should!

Cheers,
Richard

On Thu, Sep 21, 2017 at 12:51 PM, Zhang, Hong <hongzhang at anl.gov> wrote:

> Great news! According to their papers, MLSVM works only in serial. I am
> not sure what is stopping them using PETSc in parallel.
>
> Btw, are there any other cases that use PETSc for machine learning?
>
> Hong (Mr.)
>
> > On Sep 21, 2017, at 1:02 PM, Barry Smith <bsmith at mcs.anl.gov> wrote:
> >
> >
> > From: Ilya Safro isafro at g.clemson.edu
> > Date: September 17, 2017
> > Subject: MLSVM 1.0, Multilevel Support Vector Machines
> >
> > We are pleased to announce the release of MLSVM 1.0, a library of fast
> > multilevel algorithms for training nonlinear support vector machine
> > models on large-scale datasets. The library is developed as an
> > extension of PETSc to support, among other applications, the analysis
> > of datasets in scientific computing.
> >
> > Highlights:
> > - The best quality/performance trade-off is achieved with algebraic
> > multigrid coarsening
> > - Tested on academic, industrial, and healthcare datasets
> > - Generates multiple models for each training
> > - Effective on imbalanced datasets
> >
> > Download MLSVM at https://github.com/esadr/mlsvm
> >
> > Corresponding paper: Sadrfaridpour, Razzaghi and Safro "Engineering
> > multilevel support vector machines", 2017,
> > https://arxiv.org/pdf/1707.07657.pdf
> >
>
>
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