Hi Vinayak, The wiki page just lists a subset of possible topics for which candidates already showed concrete interest. I think an application for low-rank matrix completion would be more than welcome. It’s very important to work on a topic that you are interested in directly, versus just picking something from a list.
As Andy said, you should submit a proposal soon, so we can discuss and give you feedback. Some first important (but by no means complete) notes: * RecSys and matrix completion have some overlap, but they are different (and neither includes the other). I would welcome a matrix completion proposal, but RecSys are a specific end-to-end application that I believe is out of scope for scikit-learn. * I would emphasize the review of the established state-of-the art algorithms (including links to the papers and citation counts). * A batch matrix completion method (such as what the R softImpute package uses [1]) could have desirable advantages for scikit-learn inclusion (namely, using it for imputation, given the likely use case that the data fits in memory). * Such a proposal has complications from an API, metrics and cross-validation point of view, these should be discussed. Looking forward to your proposal! Yours, Vlad .[1] http://web.stanford.edu/~hastie/swData/softImpute/vignette.html > On 23 Mar 2015, at 17:11, Andreas Mueller <t3k...@gmail.com> wrote: > > Hi Vinayak. > Have you decided on your application topic? > I am trying to get a bit of an overview, and I think you haven't submitted > anything yet. > There are two other applications for the hyperparameter topic and one for the > cross-validation and gridsearch improvements. > Since Ragv is already working on cross-validation, we might prefer to give > him the topic. > > I have not looked at the hyperparameter proposals in detail, and it is > certainly fair game to put in another one. > You did a fair amount of work in the last couple days, so I'd be happy to see > a good proposal from you ;) > > I updated > https://github.com/scikit-learn/scikit-learn/wiki/Google-summer-of-code-%28GSOC%29-2015 > to reflect the current proposal status. > > Cheers, > Andy > > > > On 03/09/2015 10:48 PM, Vinayak Mehta wrote: >> Hello everyone! >> >> I'm Vinayak Mehta, an undergraduate student of computer science at Bharati >> Vidyapeeth's College of Engineering, Delhi. >> >> Since the list is not definitive, I would like to ask if the topic "Online >> Low Rank Matrix Completion" which was there in the previous revisions of the >> list, will be added again by any chance? >> >> The reason being it needs a scalable recommender system example and I am >> somewhat familiar with building a recommender system as I'm implementing one >> as my college mini project. I'm also familiar with the MovieLens dataset as >> I've built a small recommender system using it. >> >> If it will not be added, then I'll start working to understand the other two >> ideas which I think I'm interested in, "Global optimization based >> Hyperparameter optimization" and >> "Multiple metric support for cross-validation and gridsearches". >> >> Cheers! >> Vinayak Mehta (vortex_ape on freenode) >> >> >> ------------------------------------------------------------------------------ >> Dive into the World of Parallel Programming The Go Parallel Website, >> sponsored >> by Intel and developed in partnership with Slashdot Media, is your hub for >> all >> things parallel software development, from weekly thought leadership blogs to >> news, videos, case studies, tutorials and more. Take a look and join the >> conversation now. >> http://goparallel.sourceforge.net/ >> >> >> _______________________________________________ >> Scikit-learn-general mailing list >> >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > ------------------------------------------------------------------------------ > Dive into the World of Parallel Programming The Go Parallel Website, sponsored > by Intel and developed in partnership with Slashdot Media, is your hub for all > things parallel software development, from weekly thought leadership blogs to > news, videos, case studies, tutorials and more. Take a look and join the > conversation now. > http://goparallel.sourceforge.net/_______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Dive into the World of Parallel Programming The Go Parallel Website, sponsored by Intel and developed in partnership with Slashdot Media, is your hub for all things parallel software development, from weekly thought leadership blogs to news, videos, case studies, tutorials and more. Take a look and join the conversation now. http://goparallel.sourceforge.net/ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
