For simplicities sake (the actually problem is more complex)...Let say I have a set of n 3d points, whose position move over time. For all pairs, I have calculated the mean and standard deviation of the euclidean distance between them.
I would like an error metric which incorporates the following two properties and I can use to "score" each pair in an attempt to find the "best".
1) Pairs of points which on average over time are "close" to one another are preferred i.e small mean -> low error
2) Pairs of points whose distance between them over time varies little i.e small standard deviation -> low error
And I am not sure of the mathematically correct way of combining these two properties.
Any help much appreciated.