Multiway clustering via tensor block models

Miaoyan Wang, Yuchen Zeng

Abstract: 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. e demonstrate the outperformance of our approach over previous methods.

Code Link: https://cran.r-project.org/web/packages/tensorsparse/index.html

CMT Num: 365