Probabilistic Multilevel Clustering via Composite Transportation Distance

Understand

We propose a novel probabilistic approach to multilevel clustering problems based on composite transportation distance, which is a variant of transportation distance where the underlying metric is Kullback-Leibler divergence.

• Our method involves solving a joint optimization problem over spaces of probability measures to simultaneously discover grouping structures within groups and among groups.
• By exploiting the connection of our method to the problem of finding composite transportation barycenters, we develop fast and efficient optimization algorithms even for potentially large-scale multilevel datasets.
• Finally, we present experimental results with both synthetic and real data to demonstrate the efficiency and scalability of the proposed approach.