Multiway clustering via tensor block models

Advances in Neural Information Processing Systems 32 (NeurIPS 2019) 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 p...

Full description

Saved in:
Bibliographic Details
Main Authors Wang, Miaoyan, Zeng, Yuchen
Format Journal Article
LanguageEnglish
Published 10.06.2019
Subjects
Computer Science - Learning
Mathematics - Statistics Theory
Statistics - Machine Learning
Statistics - Methodology
Statistics - Theory
Online AccessGet full text

Cover

More Information
Summary:Advances in Neural Information Processing Systems 32 (NeurIPS 2019) 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.
DOI:10.48550/arxiv.1906.03807