mRMR feature selection algorithm

Proposal Title: mRMR Feature Selection Algorithm on Map-Reduce.

Student Name: Claudio Reggiani

Student E-mail: nophiq@gmail.com

Proposal Abstract:

The mRMR algorithm, described in [1], is a feature selection algorithm that leverages mutual information evaluation to select features. At each iteration, mRMR selects a new feature based on both how much it's strongly correlated to the target output and how much it's less correlated to the features already selected. The correlation is measured by means of mutual information. The project proposes to provide the mRMR algorithm in MapReduce programming framework.

Additional information:

1. The code is already available with some tests, because I'm working on my master thesis an initial milestone of my research was to implement mRMR algorithm in MapReduce.
2. I'm figuring out if it's possible for me to apply at Google Summer of Code 2013.

References:

[1] Hanchuan Peng, Fuhui Long, and Chris Ding
IEEE Transactions on Pattern Analysis and Machine Intelligence,
Vol. 27, No. 8, pp.1226-1238, 2005.
Link: http://penglab.janelia.org/papersall/docpdf/2005_TPAMI_FeaSel.pdf