Assume I have $N$ examples of datapoints with $d$ features each and I want to sample $n$ times from $N$ through a sampling function $s$, where $n \ll N$ and $d$ is the same for each datapoint.
Further assume that $s$ selects datapoints such that each new sample must have the maximum distance to the previous sample in feature space. If my goal is to maximally reduce entropy, i.e. learn the most about $N$ by sampling $n$ times, is the sampling function $s$ the best way to guarantee that? Or are there exceptions when choosing the sample that has the maximum distance to previous one in feature space might not be a good idea?
If my question is not well-defined, I appreciate any suggestion for corrections.