On Fri, Sep 22, 2017 at 12:06 AM, Richard Tran Mills <rtmi...@anl.gov> 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? > http://epubs.siam.org/doi/abs/10.1137/S1052623400374379 Matt > 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 <hongzh...@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 <bsm...@mcs.anl.gov> wrote: >> > >> > >> > From: Ilya Safro isa...@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 >> > >> >> > -- What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead. -- Norbert Wiener http://www.caam.rice.edu/~mk51/
