Feature Selection Package - Algorithms - Minimum-Redundancy-Maximum-Relevance selection (mRMR)
Description
mRMR is the scheme in feature selection is to select the features that correlate the strongest with a classification variable. This scheme, combined with selection features that are mutually different from each other while still having a high correlation make up the selection scheme of mRMR.
Usage
Method Signature:
[out] = fsMRMR(X, Y, param)

Output:
    out: A struct containing the following fields:
Input:
    X: The features on current trunk, each column is a feature vector on all instances, and each row is a part of the instance.
    Y: The label of instances, in single column form: 1 2 3 4 5 ...
    param: A struct containing the following fields (matched with their default variables). This parameter is entirely optional, and you may use only some of the fields listed below, but the defaults will be used for the remainder.
Code Example
% Using the wine.dat data set, which can be found at
% [fspackage_location]/classifiers/knn/wine.mat
fsMRMR(X,Y);
% alternatively...
parm.k = 5;
parm.pool = 50;
parm.type = -1;
fsMRMR(X,Y,parm);
Keyword in Evaluator Framework
mrmr
Paper
BibTex entry for:

eature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy.
@article { peng2005,
  author = {Peng, H. Long, F. Ding, C.},
  title = {Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy},
  journal = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE},
  volume = {27},
  number = {8},
  pages = {1226--1238},
  year = {2005}
}