Supervised Infinite Feature Selection

This is the MATLAB code for the feature selection methods described in https://arxiv.org/abs/1704.02665.

Usage

Suppose you have a n-by-m data matrix X where n is the number of examples and m the number of features, and a n-by-1 label matrix Y, To run the unsupervised mIFS, issue the following command:

>> [RANKED, WEIGHT] = mIFS( X,alpha )

To run the supervised SIFS, issue the following command:

>> [RANKED, WEIGHT] = SIFS( X,Y,alpha )

In both commands, alpha is a coefficient ranging from 0 to 1. This parameter can be set using cross-validation on training data X.

Remarks:

1. To use mIFS, we recommend normalizing the data using the following commands (see the paper for details):

   >> R = bsxfun( @minus, X, min(X));
   >> R = bsxfun( @rdivide, dataR, max(dataR));
   >> features_kept = find( isnan( sum(dataR) ) | isinf( sum(dataR) ) );
   >> R(:,features_kept)=0;
   >> X=dataR;
   

2. To use SIFS, we recommend standardizing the data using the following command (see the paper for details):

   >> X = zscore(trainX);  % Note that the same zscore transformation should be applied to test data