FSS - feature subset selection and feature ranking 
   the method is motivated by the max-relevance-min-redundancy (mRMR) 
   approach [1]. However, the default method uses partial correlation,
   which has been developed from scratch. PCCM [3] describes
   a similar idea, but is more complicated. 
   An alternative method based on FSDD is implemented, too. 
    
  [idx,score] = fss(D,cl) 
  [idx,score] = fss(D,cl,MODE) 
  [idx,score] = fss(D,cl,MODE) 
    
 D 	data - each column represents a feature 
 cl	classlabel   
 Mode 	'Pearson' [default] correlation
	'rank' correlation 
       'FSDD' feature selection algorithm based on a distance discriminant [2]
       %%% 'MRMR','MID','MIQ' max-relevance, min redundancy [1] - not supported yet. 

 score score of the feature
 idx	ranking of the feature    
       [tmp,idx]=sort(-score)

 see also: TRAIN_SC, XVAL, ROW_COL_DELETION

 REFERENCES:
 [1] Peng, H.C., Long, F., and Ding, C., 
   Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy, 
   IEEE Transactions on Pattern Analysis and Machine Intelligence, 
   Vol. 27, No. 8, pp.1226-1238, 2005.
 [2] Jianning Liang, Su Yang, Adam Winstanley, 
   Invariant optimal feature selection: A distance discriminant and feature ranking based solution, 
   Pattern Recognition, Volume 41, Issue 5, May 2008, Pages 1429-1439.
   ISSN 0031-3203, DOI: 10.1016/j.patcog.2007.10.018.
 [3] K. Raghuraj Rao and S. Lakshminarayanan
   Partial correlation based variable selection approach for multivariate data classification methods
   Chemometrics and Intelligent Laboratory Systems
   Volume 86, Issue 1, 15 March 2007, Pages 68-81 
   http://dx.doi.org/10.1016/j.chemolab.2006.08.007

Package: nan