I have a pool of thousands of binary vectors, all of the same length l.
I would like to sort them, by their correlation/similarity to a test vector v that also has length of l.
Also the vectors (test vector and the binary vector) are sparse - containing a majority of over 90% zeros while the test vector could be less sparse but also above 70%
Condensed example, v is the test vector:
v = [-1, 5, 0, 0, 10, 0, -7]
v1 = [1, 0, 0, 0 ,0 ,0 ,0]
v2 = [0, 1, 0, 0 ,1 ,0 ,0]
v3 = [1, 1, 0, 0 ,0 ,0 ,1]
I would expect to get here v2 for first place and and v3 for second, the reason why I want v3 to be second is that -7 matching 0 is more significant to 5 matching 1.
The logic behind what I'm looking for is that the higher the positive value is the more similar it would be for a 1 and the more negative a value is the more similar it would be to a 0.
Is there any method/correlation that is suited for this kind of purpose?
