Hello! In my opinion the ideas page needs a bit of restructuring, as the breadth and complexity of the suggested projects varies.
We are definitely welcome to different ideas, as long as they prove useful and fit in the big picture for where the scikit is heading. This comes down to discussing it on the mailing list, so you did the right thing! I am personally not familiar with the algorithm you are talking about, could you please provide a reference paper? We have a semi-strict guideline on only considering algorithms that have many citations; I don't recall at the moment how many does "many" mean. Again, I don't know the algorithm, but I tend to think that a single algorithm is a contribution that is too small for a whole summer. I also think it would be better to have the standard SGD matrix factorization algorithm before implementing (what I suppose are) variants of it. But I don't want to discuss this further before looking at the paper, since I'm not standing on solid ground. I am excited for your interest in this. Remember, the first step, no matter what will be the project you would like to propose, is to start contributing one or several minor improvements or fixes, in order to get familiar with the codebase and with the way our development process works. Cheers, Vlad On Mon, Apr 8, 2013 at 11:40 AM, YanChunwei <[email protected]> wrote: > Hello, I am a computer science graduate student，and want to apply for > scikit-learn's GSoC2013. > I have a question, are all scipy-learn's tasks for GSOC's applicants the > topics listed on the project's wiki on github? > https://github.com/scikit-learn/scikit-learn/wiki/A-list-of-topics-for-a-Google-Summer-of-Code-%28GSOC%29-2013。 > > I list some topics here: > > Add scipy.sparse matrix input support to the Decision Tree Implementation > Online Low Rank Matrix Completion > Online Non Negative Matrix Factorization > ... ... > > > Is that possible for an applicant to implement other ideas? > > I have an idea, to implement a new algorithm for scikit-learn, the > Feature-Based Matrix Factorization (FBMF). It is powerful and should have > a long-term application in the field of Collaborative Filter. > > Similar to Factorization Machine , this model is an abstract of many > variants of matrix factorization models, and new types of information can > be utilized by simply defining new features, without modifying any lines of > code. > > To build an open source implementation and integrate it into the > scikit-learn, and use the rich algorithms provided by scipy-learn to > automatically extract features, using python to control the overall > training process (automatically grid search and so on), I think that is a > wonderful choice. > > Is there anyone else think it is an good idea, and provide some suggestions > for me to participate in GSOC of scipy-learn, I really appreciate it. > > Chunwei Yan > > ------------------------------------------------------------------------------ > Minimize network downtime and maximize team effectiveness. > Reduce network management and security costs.Learn how to hire > the most talented Cisco Certified professionals. Visit the > Employer Resources Portal > http://www.cisco.com/web/learning/employer_resources/index.html > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ Minimize network downtime and maximize team effectiveness. Reduce network management and security costs.Learn how to hire the most talented Cisco Certified professionals. Visit the Employer Resources Portal http://www.cisco.com/web/learning/employer_resources/index.html _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
