Alternating Direction Method of Multipliers for Generalized Low-Rank Tensor Recovery

Alternating Direction Method of Multipliers for Generalized Low-Rank Tensor Recovery

Low-Rank Tensor Recovery (LRTR), the higher order generalization of Low-Rank Matrix Recovery (LRMR), is especially suitable for analyzing multi-linear data with gross corruptions, outliers and missing values, and it attracts broad attention in the fields of computer vision, machine learning and data...

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Bibliographic Details
Main Authors: Jiarong Shi, Qingyan Yin, Xiuyun Zheng, Wei Yang
Format: Article
Language:English
Published: MDPI AG 2016-04-01
Series:Algorithms
Subjects:
low-rank tensor recovery
low-rank matrix recovery
nuclear norm minimization
alternating direction method of multipliers
Online Access:http://www.mdpi.com/1999-4893/9/2/28

Internet

http://www.mdpi.com/1999-4893/9/2/28

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