pluslr.m

一个用matlab写的特征挑选

%----4.18编 增 l减 r法  特征选择 
clear;
clc;
%--------特征导入  请自行修改
M=256;N=256;
load coourfeature_0420_FGL-5 %%%共生矩阵 96.14％
feature{1}=coourfeature(:,1);
feature{2}=coourfeature(:,2);
feature{3}=coourfeature(:,3);
load fufeature_0420_FGL-5 %%复小波  98.26％
for i=1:13
    feature{3+i}=wavefeature(:,i);
end
load wavefeature_feixia_0420_FGL-5%%%非下采样小波  97.58％
for i=1:7
    feature{16+i}=wavefeature(:,i);
end
load wavefeature_0420_FGL-5%%小波 97.65％
for i=1:7
    feature{23+i}=wavefeature(:,i);
end
%%%%%%%----------归一化
[m n]=size(feature{1});
for j=1:30%一共30组特征 这里 请自行修改
    mx=max(feature{j});
    mi=min(feature{j});
    mxx=(mx-mi);
    mii=ones([m n])*mi;
    feature{j}=(feature{j}-mii)./mxx;
end
%-------------------------特征选择开始啦!!
%-说明:本算法是l>r,就是自下而上的计算

l=4;r=3;d=1;%d 是最终要得到的特征数 l是每次增加的特征 r是每次减去的特征 
chosen=[];%%表示已选的特征
chosen=[chosen 1];
while d<12
    %-----------------------------1 选l个特征-------------------------------------
    for j=1:l   %选l个特征
        J=zeros([1 30]);
        for i=1:30  %%一共30组特征 这里 请自行修改 
           [mm nn]=size(chosen);
            for p=1:nn
                 if i==chosen(p)
                    J(i)=0;
                    break;   
                 else
                      J(i)=J(i)-sum(sum((feature{i}-feature{chosen(p)}).^2));          
                 end
            end
        end
        mi=min(J);
        for i=1:30
            if J(i)==0
               J(i)=mi;     
            end
        end
        [ma1 we1]=max(J);
        chosen=[chosen we1];             
    end
%-----------------------------2 去掉r个特征-------------------------------------
   
    dele=[];
   for j=1:r   %%删r个
       [ch chnum]=size(chosen);
      J=zeros([1 chnum]);ii=0;  
      for i=1:chnum  %     去掉chosen中第i个的效果
      [mm nn]=size(chosen);
        for p=1:nn
            
            
              for q=1:nn    
                  if (chosen(q)~=chosen(i)) & (chosen(p)~=chosen(i))             
                    J(i)=J(i)-sum(sum((feature{chosen(q)}-feature{chosen(p)}).^2));  
                  end
              end
           
        end
      end
%        mi=min(J);
       for cc=1:chnum
            if J(cc)==0
                J(cc)=mi;
            end
       end
       [ma we]=max(J);  
        chosen(we)=[];
                
    end
    d=d+l-r
end

[mm nn]=size(chosen);
tezh=[];
for i=1:nn
    tezh=[tezh feature{chosen(i)}];
end
% %%%%%%%%聚类
% [IDC,U]=kmeans(tezh,4);
%        cc(IDC==1,1)=0;
%        cc(IDC==2,1)=0.25;
%        cc(IDC==3,1)=0.5;
%        cc(IDC==4,1)=0.75;
% % g=reshape(cc,M,N); 
% %  figure,imshow(g);
%  save 0423gFGL g