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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?

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  • $\begingroup$ What is the formula behind "-7 matching 0 is more significant than (?) 5 matching 1"? This is not a normal correlation. $\endgroup$
    – Gijs
    Jan 23 '17 at 10:41
  • $\begingroup$ I know it's not that why I am having a problem with the solution... Simply put I want the bigger the absolute value is in the test vector to have more significance in the correlation calculation where negative values match zeros and positive to 1 - the example with -7 and +5 is to show that I want that -7 matching 0 is "stronger" than +5 matching 1 $\endgroup$ Jan 23 '17 at 11:55
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Absolute value of Pearson correlation solved this problem

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