featselmargdif.m

用于matlab环境下的支持向量机svm的工具箱

function [RankedVariables,nbsvvec,Values]=FeatSelmargdif(x,y,c,kernel,kerneloption,verbose,span,FeatSeloption)

% Usage
%  
%   [RankedVariables,nbsvvec,Values]=FeatSelmargdif(x,y,c,kernel,kerneloption,verbose,span,FeatSeloption)
%
%
%   x,y     : input data
%   c       : penalization of misclassified examples
%   kernel  : kernel type
%   kerneloption : kernel hyperparameters
%   verbose
%   span    : matrix for semiparametric learning
%   FeatSeloption : structure containing FeatSeloption parameters
%           Fields           
%
%           AlphaApprox : O for retraining,  1 for approximation 
%           RemoveChunks : number of variable to remove (a number or 'half')
%           StopChunks   : remove 1 variable at a time when number of variables reaches this value
%           FirstOrderMethod : how to calculate the derivatives
%               'grad','scal', 'absgrad', 'absscal'
%
%
%
% [e-mail]
%   
%   \bibitem[Rakotomamonjy(2002)]{rakoto_featsel}
%    A.~Rakotomamonjy.
%   \newblock Variable selection using svm based criteria.
%   \newblock Technical Report 02-004, Insa de Rouen Perception Syst\`eme
%   Informations, http://asi.insa-rouen.fr/\char126arakotom, 2002.
%
%

if nargin <8
    FeatSeloption.AlphaApprox=1;
end;

%----------------------------------------------------------%
%              Testing Fields Existence                    % 
%----------------------------------------------------------%

if ~isfield(FeatSeloption,'AlphaApprox')
    FeatSeloption.AlphaApprox=1;
end;
if ~isfield(FeatSeloption,'RemoveChunks')
    FeatSeloption.RemoveChunks=1;
end;
if ~isfield(FeatSeloption,'StopChunks')
    FeatSeloption.StopChunks=10;
end;
if ~isfield(FeatSeloption,'FirstOrderMethod')
   FeatSeloption.FirstOrderMethod='grad';
end;

if ~isfield(FeatSeloption,'Nbkeep')
   FeatSeloption.Nbkeep=0;
end;

if strcmp(FeatSeloption.RemoveChunks,'half')
    half=1;
else
    half=0;
end;
%----------------------------------------------------------%
%              Initialization                 % 
%----------------------------------------------------------%
SelectedVariables = [1:size(x,2)]; %list of remaining variable
EliminatedVariables = []; %list of eliminated variables
Values=[];
caux=diag((1/c)*ones(length(y),1));
alphaall=[];
betaall=[];
nbsvvec=[];


while length(SelectedVariables)>FeatSeloption.Nbkeep
    if half==1
        FeatSeloption.RemoveChunks=round(length(SelectedVariables)/2);
    end;
    
    if FeatSeloption.RemoveChunks<=FeatSeloption.StopChunks/2 & half == 1
        FeatSeloption.RemoveChunks=1;
    end;
    
    if length(SelectedVariables)<=FeatSeloption.StopChunks
        FeatSeloption.RemoveChunks=1;
    end;
    
      
    xaux=x(:,SelectedVariables);
    ps=svmkernel(xaux,kernel,kerneloption);
    
    lambda=1e-7;
    psc=ps+caux;
    H =psc.*(y*y'); 
    e = ones(size(y));
    A = y;
    b = 0;
    
    
    %-------- This is a QP algorithm that should be replaced by your own QP ---------
    [alpha , lambda , pos] =  monqpCinfty(H,e,A,b,lambda,verbose,x,psc,alphaall); 
    %--------------------------------------------------------------------------------
    
    nbsv=length(pos);
    nbsvvec=[nbsv nbsvvec];
    alphaall=zeros(size(e));
    alphaall(pos)=alpha;
     w2=2*(-0.5*alpha'*H(pos,pos)*alpha +e(pos)'*alpha);

    SelectVariablesAux=SelectedVariables;
    margdif=[];
  
    psaux=ps(pos,pos);  
    SelectVariablesAux=SelectedVariables;
    for i=1:length(SelectedVariables)
          xnon2= x(pos,SelectVariablesAux(i)); 
          xpos=x(pos,:);
        switch FeatSeloption.FirstOrderMethod
            
        case 'absgrad'    
             
              [kernelderiv_1,kernelderiv_2]=featselkernelderivative(psaux,xnon2,kernel,kerneloption,'grad',xpos);
              gradmarg_1= -(y(pos).*alphaall(pos))'*abs(kernelderiv_1)* (y(pos).*alphaall(pos));
              gradmarg_2= -(y(pos).*alphaall(pos))'*abs(kernelderiv_2)* (y(pos).*alphaall(pos));
              margdif(i)=gradmarg_1.^2+gradmarg_2.^2;
        case 'grad'    
              [kernelderiv_1,kernelderiv_2]=featselkernelderivative(psaux,xnon2,kernel,kerneloption,'grad',xpos);
              gradmarg_1= -(y(pos).*alphaall(pos))'*kernelderiv_1* (y(pos).*alphaall(pos));
              gradmarg_2= -(y(pos).*alphaall(pos))'*kernelderiv_2* (y(pos).*alphaall(pos));
              margdif(i)=gradmarg_1.^2+gradmarg_2.^2;
              
        case 'scal'
              [kernelderiv_1,kernelderiv_2]=featselkernelderivative(psaux,xnon2,kernel,kerneloption,'scal',xpos);
              gradmarg_1= (y(pos).*alphaall(pos))'*kernelderiv_1* (y(pos).*alphaall(pos)); % suppressed a minus sign AR 04/06/03
              margdif(i)=gradmarg_1.^2;
             

        case 'absscal'
              [kernelderiv_1,kernelderiv_2]=featselkernelderivative(psaux,xnon2,kernel,kerneloption,'scal',xpos);
              gradmarg_1= -(y(pos).*alphaall(pos))'*abs(kernelderiv_1)* (y(pos).*alphaall(pos));
              margdif(i)=gradmarg_1.^2;
              
        otherwise
            error(' Feature Selection First Order Method is undefined...');
        end;
        
    end

    [nointerest indiceDJ] = sort(margdif);
    EliminatedVariables = [SelectedVariables(indiceDJ(1:FeatSeloption.RemoveChunks)) EliminatedVariables];
    Values= [margdif(indiceDJ(1:FeatSeloption.RemoveChunks)) Values];
    SelectedVariables(indiceDJ(1:FeatSeloption.RemoveChunks)) = [];
    
end;

RankedVariables=[ SelectedVariables EliminatedVariables ];