In this article, Wang, Huang, Kamangar, Nie, and Ding discuss a new algorithm for multi-instance learning.
“In MIL data objects are represented as bags of instances, therefore the distance between the objects is a set-to-set distance. Compared to traditional single-instance data that use vector distance such as Euclidean distance, estimating the Bag-to-Bag (B2B) distance in MIL is more challenging [7,8]. In addition, the B2B distances often do not truly reﬂect the semantic similarities .”
I am hoping to apply these ideas to astronomical image stacking.