I am so confused. I have tested a program for myself by following MATLAB code :
feature_train=[1 1 2 1.2 1 1 700 709 708 699 678];
No_of_Clusters = 2;
No_of_Iterations = 10;
[m,v,w]=gaussmix(feature_train,[],No_of_Iterations,No_of_Clusters);
feature_ubm=[1000 1001 1002 1002 1000 1060 70 79 78 99 78 23 32 33 23 22 30];
No_of_Clusters = 3;
No_of_Iterations = 10;
[mubm,vubm,wubm]=gaussmix(feature_ubm,[],No_of_Iterations,No_of_Clusters);
feature_test=[2 2 2.2 3 1 600 650 750 800 658];
[lp_train,rp,kh,kp]=gaussmixp(feature_test,m,v,w);
[lp_ubm,rp,kh,kp]=gaussmixp(feature_test,mubm,vubm,wubm);
However, the result is wondering me because the feature_test must be classified in feature_train not feature_ubm. As you see below the probability of feature_ubm is more than feature_train!?! Can anyone explain for me what is the problem ? Is the problem related to gaussmip and gaussmix MATLAB functions ?
sum(lp_ubm)
ans =
-3.4108e+06
sum(lp_train)
ans =
-1.8658e+05
As you see below the probability of feature_ubm is more than feature_train!?!
You see exactly the opposite, despite the absolute value of ubm is big, you are considering negative numbers and
sum(lp_train) > sum(lp_ubm)
hense
P(test|train) > P(test|ubm)
So your test chunk is correctly classified as train, not as ubm.