Poster
Multiway clustering via tensor block models
Miaoyan Wang · Yuchen Zeng

Wed Dec 11th 05:00 -- 07:00 PM @ East Exhibition Hall B + C #98

We consider the problem of identifying multiway block structure from a large noisy tensor. Such problems arise frequently in applications such as genomics, recommendation system, topic modeling, and sensor network localization. We propose a tensor block model, develop a unified least-square estimation, and obtain the theoretical accuracy guarantees for multiway clustering. The statistical convergence of the estimator is established, and we show that the associated clustering procedure achieves partition consistency. A sparse regularization is further developed for identifying important blocks with elevated means. The proposal handles a broad range of data types, including binary, continuous, and hybrid observations. Through simulation and application to two real datasets, we demonstrate the outperformance of our approach over previous methods.

Author Information

Miaoyan Wang (University of Wisconsin - Madison)
Yuchen Zeng (University of Wisconsin - Madison)