# Machine learning analysis of differences

I'm very new to machine learning, and have a problem I'm trying to solve but don't know where to get started.

I've got a set of feature vectors that are "matched pairs," so

D={(x1,y1), (x2,y2), ..., (xn,yn)}


where each xi and yi are vectors of some length. xi and yi have the same length, but different pairs of xi and yi may have different lengths.

I want to have some algorithm learn the difference of x's and y's, such that, if I have another vector, I want to predict whether it's more like x's or more like y's.

Can someone point me in the right direction that I might try? I'm not sure what types of algorithms or methods I should be looking into, so even just the names of appropriate algorithms or methods would be great.

• This seems a little unusual. Can you provide a simple example? – gung - Reinstate Monica Jan 16 '16 at 1:28
• An example would be a study of genetic mutations. So x's and y's are genetic sequences, where y is a mutation of x. For example: x1=ACCTGG, and y1=GGCTGG, so the "AC" in x1 mutates to become "GG" in y1. In this example, I would be trying to find if there is some pattern in the mutation so that I can determine if a new sequence is likely to be x (unmutated) or y (mutated). – leontp587 Jan 16 '16 at 17:54

What you are asking is not ML. In your case, if you have a lot of $x$'s and $y$'s, then simply determine the correlation matrix for all pairwise combinations of all of your $x$'s and $y$'s. Then look at the values and statistical significance of each correlation coefficient. Your question really is asking if a given $y$ is associated more strongly with a given $x$.