Support Vectors are wrong in my SVR model

I trained an SVR model by fitrsvm. Since I need to get all support vector I have checked the model ouput. Surprisingly, the mdl.SupportVectors have nothing in common with my input mdl.X. Can anyone tell me why.

Below is my code. sorry the csv files are too larged to be uploaded.

    input = csvread('sample.csv');
  target = csvread('target.csv');
  Y = log(target(2,:)+1)';
  %Y = log(target(2,:)+1)';
  %turbidity = log(target(1,:)+1);
  turbidity = target(1,:);
  mainComp = 15;
  s = load('eigenVector.mat','s');
  s = s.s;
  v = load('singularValue.mat','v');
  v = v.v;

a = s'*input;
b = inv(v(1:mainComp,1:mainComp))*a(1:mainComp,:); 
X = [b;log(turbidity+1)];
mdl = fitrsvm(X',Y,'Standardize',true,'KernelFunction','polynomial','KernelScale','auto','OutlierFraction',0.05);
save mySVMMDL mdl;
yfit = exp(predict(mdl,X'))-1;

Answer 1

[Jo]

Hello,

I know this post is old but maybe my answer can help someone that may have the same question ?

The data stored in "mdl.SupportVectors" are preprocessed data. As you have set the option 'standardized','true', these data are standardized. So you need to standardized manually "Mdl.X" to be able to compare both matrices.