Two methods for encoding clusters
This paper presents two methods for generating numerical codes representing clusters of Rn, while preserving various topological properties of data spaces. This is useful for networks whose input, or eventually output, consists of unordered sets of points. The first method is the best one from a theoretical point of view, while the second one is more usable for large clusters in practice.
unordered sets cluster codes neural network input and output representations pseudo-sequences encoding problem