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Title: Multiway clustering via tensor block models
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.  more » « less
Award ID(s):
1915978
NSF-PAR ID:
10158228
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Advances in Neural Information Processing Systems 32 (NeurIPS)
Volume:
32
Page Range / eLocation ID:
715-725
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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