The Annals of Statistics

Cross: Efficient low-rank tensor completion

Anru Zhang

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Abstract

The completion of tensors, or high-order arrays, attracts significant attention in recent research. Current literature on tensor completion primarily focuses on recovery from a set of uniformly randomly measured entries, and the required number of measurements to achieve recovery is not guaranteed to be optimal. In addition, the implementation of some previous methods are NP-hard. In this article, we propose a framework for low-rank tensor completion via a novel tensor measurement scheme that we name Cross. The proposed procedure is efficient and easy to implement. In particular, we show that a third-order tensor of Tucker rank-$(r_{1},r_{2},r_{3})$ in $p_{1}$-by-$p_{2}$-by-$p_{3}$ dimensional space can be recovered from as few as $r_{1}r_{2}r_{3}+r_{1}(p_{1}-r_{1})+r_{2}(p_{2}-r_{2})+r_{3}(p_{3}-r_{3})$ noiseless measurements, which matches the sample complexity lower bound. In the case of noisy measurements, we also develop a theoretical upper bound and the matching minimax lower bound for recovery error over certain classes of low-rank tensors for the proposed procedure. The results can be further extended to fourth or higher-order tensors. Simulation studies show that the method performs well under a variety of settings. Finally, the procedure is illustrated through a real dataset in neuroimaging.

Article information

Source
Ann. Statist., Volume 47, Number 2 (2019), 936-964.

Dates
Received: November 2016
Revised: November 2017
First available in Project Euclid: 11 January 2019

Permanent link to this document
https://projecteuclid.org/euclid.aos/1547197244

Digital Object Identifier
doi:10.1214/18-AOS1694

Mathematical Reviews number (MathSciNet)
MR3909956

Zentralblatt MATH identifier
07033157

Subjects
Primary: 62H12: Estimation
Secondary: 62C20: Minimax procedures

Keywords
Cross tensor measurement denoising minimax rate-optimal neuroimaging tensor completion

Citation

Zhang, Anru. Cross: Efficient low-rank tensor completion. Ann. Statist. 47 (2019), no. 2, 936--964. doi:10.1214/18-AOS1694. https://projecteuclid.org/euclid.aos/1547197244


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Supplemental materials

  • Supplement to “Cross: Efficient low-rank tensor completion”. In the supplement, we provide proofs for the main results and technical lemmas. For better presentation for the long proof of Theorem 2, we also provide a table of used notation.