Jakub,
This is great, thanks for the information. I've added links from the PETSc
main webpage to your work.
Barry
> On Sep 23, 2017, at 9:26 AM, Jakub Kruzik <[email protected]> wrote:
>
> Dear all,
>
> I would just like to note that we also develop SVM implementation. It is
> intended for large-scale datasets and makes use of PETSc parallel linear
> algebra. Currently, it supports only linear kernels - Hessian is, in fact,
> MATNORMAL with arbitrary underlying data matrix - it is, e.g. possible to use
> MATDENSE or MATAIJ depending on the problem. For the solution of the arising
> quadratic program (QP), it uses solvers from our PermonQP package. Both
> PermonSVM and PermonQP are libraries depending on PETSc. They are written in
> the PETSc coding style, pretty much like SLEPc.
>
> http://permon.it4i.cz/permonqp.htm
> http://permon.it4i.cz/permonsvm.htm
>
> https://github.com/it4innovations/permon
> https://github.com/it4innovations/permonsvm
>
> So far, PermonQP only implements an Augmented Lagrangian type algorithm which
> can be combined with any solver for box-constrained QP. In PermonQP, there
> are some concrete ones and also TAO wrapper. However, adding an Interior
> Point implementation is interesting for us as well.
>
> PermonSVM is so far a proof-of-concept thing, but it already scales pretty
> well (almost proportionally to the application of the data matrix to a
> vector). See, e.g. our PASC poster
> https://www.researchgate.net/publication/318317204_PERMON_PASC17_Poster
>
> We'll be grateful for any feedback on this.
>
> Jakub
>
>
> On 22.9.2017 06:06, Richard Tran Mills wrote:
>> 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 <[email protected]> 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 <[email protected]> wrote:
>> >
>> >
>> > From: Ilya Safro [email protected]
>> > 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
>> >
>>
>>
>
