I am trying to implement the convex optimization problem in the link:
As I understand the last condition 1m means identity matrix. May I ask if the following code in CVX (matlab is fine)
E=zeros(NumClasses*TrainSample,NumClasses); vecOnes=ones(L,1); E(1:L,1)=vecOnes; E(L+1:2*L,2)=vecOnes; E(2*L+1:3*L,3)=vecOnes; E(3*L+1:4*L,4)=vecOnes; Q=TrainFeat'*TrainFeat; gamma=.5 cvx_begin variable U(NumClasses*TrainSample,NumClasses) maximize(-1/2*(U(:,1)'*Q*U(:,1)+U(:,2)'*Q*U(:,2)+U(:,3)'*Q*U(:,3)+U(:,4)'*Q*U(:,4))+(E(:,1)'*U(:,1)+E(:,2)'*U(:,2)+E(:,3)'*U(:,3)+E(:,4)'*U(:,4))) subject to U-gamma*E<=0 U*eye(4)==0 cvx_end
In the above Q is kernel defined as X’X where X is the data matrix. Is the last condition Ueye(4)==0 necessary?
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