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 ImplementationOnline Low Rank Matrix CompletionOnline 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
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