HI. here's how you should do it:
First i create a sample database for my self to use as parameters: (i use 100 points in 4D feature space with 3 label, change every part for your problem)
N = 100; % number of points
D = 4; % Dimension of feature space
Class_count = 3; % number of Classes
%% Create random dataset for parametrization
X = rand(N,D); % Position of Points in feature space
Y = randi([1 Class_count],N,1); % Label of Points
Now let us create Matrix of Coefficients A, right-hand-side vector B and Cost weights f.
f = [zeros(1,D) 1];
A = zeros(N^2,D+1);
b = zeros(N^2,1);
for i=1:N
for j=1:N
I = (i-1)*N + j;
if Y(i)==Y(j)
A(I,:) = [((X(i,:)-X(j,:)).^2) -1];
b(I) = 0;
else
A(I,:) = [-((X(i,:)-X(j,:)).^2) 0];
b(I) = -1;
end
end
end
lb = zeros(D+1,1); % lower bound
Now time to use linprog:
Solution = linprog(f,A,b,[],[],lb,[]) % no equality constraint and no upper bound
W = Solution(1:D)
