Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 115 - Advances in Clustering and Classification
Type: Contributed
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #323243
Title: Multiway Spherical Clustering via Degree-Corrected Tensor Block Models
Author(s): Jiaxin Hu* and Miaoyan Wang
Companies: University of Wisconsin-Madison and University of Wisconsin-Madison
Keywords: tensor clustering; degree correction; statistical-computational efficiency; human brain connectome networks
Abstract:

We consider the problem of multiway clustering in the presence of unknown degree heterogeneity. Such data problems arise commonly in applications such as recommendation system, neuroimaging, community detection, and hypergraph partitions in social networks. The allowance of degree heterogeneity provides great flexibility in clustering models, but the extra complexity poses significant challenges in both statistics and computation. Here, we develop a degree-corrected tensor block model with estimation accuracy guarantees. We present the phase transition of clustering performance based on the notion of angle separability, and we characterize three signal-to-noise regimes corresponding to different statistical-computational behaviors. In particular, we demonstrate that an intrinsic statistical-to-computational gap emerges only for tensors of order three or greater. Further, we develop an efficient polynomial-time algorithm that provably achieves exact clustering under mild signal conditions. The efficacy of our procedure is demonstrated through two data applications, one on human brain connectome project, and another on Peru Legislation network dataset.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program