Distributed spectral clustering based on Euclidean distance matrix completion

In this paper, we consider the problem of distributed spectral clustering, wherein the data to be clustered is (horizontally) partitioned over a set of interconnected agents with limited connectivity. In order to solve it, we consider the equivalent problem of reconstructing the Euclidean distance matrix of pairwise distances among the joint set of datapoints. This is obtained in a fully decentralized fashion, making use of an innovative...