Matlab feature selection - matlab

Question

I am trying to learn relevant features in a 300*299 training matrix by taking a random row from it as my test data and applying sequentialfs on it. I have used the following code:
>> Md1=fitcdiscr(xtrain,ytrain);
>> func = #(xtrain, ytrain, xtest, ytest) sum(ytest ~= predict(Md1,xtest));
>> learnt = sequentialfs(func,xtrain,ytrain)
xtrain and ytrain are 299*299 and 299*1 respectively. Predict will give me the predicted label for xtest(which is some random row from original xtrain).
However, when I run my code, I get the following error:
Error using crossval>evalFun (line 480)
The function '#(xtrain,ytrain,xtest,ytest)sum(ytest~=predict(Md1,xtest))' generated the following error:
X must have 299 columns.
Error in crossval>getFuncVal (line 497)
funResult = evalFun(funorStr,arg(:));
Error in crossval (line 343)
funResult = getFuncVal(1, nData, cvp, data, funorStr, []);
Error in sequentialfs>callfun (line 485)
funResult = crossval(fun,x,other_data{:},...
Error in sequentialfs (line 353)
crit(k) = callfun(fun,x,other_data,cv,mcreps,ParOptions);
Error in new (line 13)
learnt = sequentialfs(func,xtrain,ytrain)
Where did I go wrong?

Answer 1

You should build your classifier inside func, not before.
sequentialfs calls the function each time on different sets, and a classifier must be built specifically for each set, using only the features sequentialfs selected for that iteration.
I'm not sure I managed to be clear, in practice you should move the first line of your code inside the body of func
Source: MathWorks