2019/09/19 by Viet Huynh, Huynh, Viet, Nhat Ho +11 · 1 citation
Computer Science · Engineering · #Advanced Clustering Algorithms Research #Anomaly Detection Techniques and Applications #Automated Road and Building Extraction #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.1909.08787
openalex publication_date 2019/09/19 · openalex created_date 2019/09/26 · openalex updated_date 2026/07/28
We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a potentially large hierarchically structured corpus of data. Our method involves a joint optimization formulation over several spaces of discrete probability measures, which are endowed with Wasserstein distance metrics. We propose several variants of this problem, which admit fast optimization algorithms, by exploiting the connection to the problem of finding Wasserstein barycenters. Consistency properties are established for the estimates of both local and global clusters. Finally, experimental results with both synthetic and real data are presented to demonstrate the flexibility and scalability of the proposed approach.