I have a list of m x n similarity score matrix, something like
c1 c2 c3 c4 c5 d1 0.2159824 0.3528572 0.2390016 0.3673485 0.2849448 d2 0.2849448 0.2669695 0.2441495 0.3829949 0.3511353 d3 0.3281100 0.3251407 0.4328260 0.2895179 0.2814589
these "similarity scores" lie in between 0-1. What I am trying to do here is to combine these scores into a single score, also in between 0-1.
My issue here is that I am not able to figure out a good approach to combine these scores into this single score. So far I have tried taking the average, max. value, calculating row, column averages and using the max.value out of them. The problem with these scores is that the matrices I have vary a lot in row and column lengths, and I cannot account for this variation using average because at the end of the day I have to sort these matrices based on this similarity score and select n top ranking ones, and from manually checking these observations, I realized that max.value in a matrix is not a suitable single score the similarity between these observations. Do you have any suggestions for an approach to combine these scores ?
Also is there any statistical tests that could be applied on this combined score ? I have tried random sampling approach, but the steps to calculate similarity scores for the observations take a long time to run and iterating these steps ~1,000 times or more is not feasible now.