CROSS: EFFICIENT LOW-RANK TENSOR COMPLETION

• 成果类型: Article
• 署名作者: Zhang, Anru
• 署名单位: University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/18-AOS1694
• 发表日期: 2019
• 页码: 936-964
• 关键词: matrix completion; Optimal Rates; NORM; RECOVERY; reconstruction; dimensionality; decompositions

摘要

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 mini-max 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.