How can I measure separability between different number of instance of one feature vector - statistics

Question

How can I measure separability between different number of instance of one feature vector ?
For example the main vector is V=[1 1 2 3 4 5 7 8 10 100 1000 99 999 54] and different combination with different sample lengths are
t1=[1 1 2 3 99 1000] or t2=[1 10 1000] or t3=[2 3 4 10 100 99 999 54]
which one is more separable and more informative ?
If I put it in GMM, the vectors with less samples has better probability which is not fair.
train=[1 2 1 2 1 2 100 101 102 99 100 101 1000 1001 999 1003];
No_of_Iterations=10;
No_of_Clusters=3;
[mm,vv,ww]=gaussmix(train,[],No_of_Iterations,No_of_Clusters);
test1=[1 1 1 2 2 2 100 100 100 101 1000 1000 1000];
test2=[1 1 2 2 100 99 1000 999];
test3=[1 100 1000];
[lp,rp,kh,kp]=gaussmixp(test1,mm,vv,ww);
sum(lp)
[lp,rp,kh,kp]=gaussmixp(test2,mm,vv,ww);
sum(lp)
[lp,rp,kh,kp]=gaussmixp(test3,mm,vv,ww);
sum(lp)
The results are as follow :
ans =
-8.0912e+05
ans =
-8.1782e+05
ans =
-5.0381e+05
I will really appreciate, if you could help me.

Answer 1

How can I measure separability between different number of instance of one feature vector ?
Notion of "separability" is not strict. If data is linearly separable one could define the size of the margin as the "separability", but in case of not linarly separable data there is no definite answer even for question "how easy is to separate this data", as it is heavily model dependent question - the answer will be completely differennt if you want to separate it with SVM with some partciular kernel and different if you want to use a decision tree etc.. There are many possible probabilistic, geometric and statistical approaches to such analysis, but this is not the Q&A site place, this is hard and long-lasting process od data analysis, which is performed by skilled researchers.
which one is more separable and more informative ?
Depends on the exact definition of separability and informativity. This is not a question that can be answered in the Q&A fashion, this is a research topic, not an issue to solve.
If I put it in GMM, the vectors with less samples has better probability which is not fair.
You have already asked the question about it and received answer showing why it is "fair".
You can try to ask on http://stats.stackexchange.com but you will rather hear similar answer - that "it depends" and is impossible to answer such a question "by hand".