Recovery of Corrupted Low-Rank Tensors

• 作者: Gangyao Kuang Haiyan Fan
• 来源期刊: 应用数学(英文)
• 学科: 医学
• 关键词: Low-Rank TENSOR TENSOR RECOVERY AUGMENTED Lagrangian Method Impulsive Noise Mixed Noise
• 年，卷（期）: 2017,（2）
• 页码范围: 229-244
• 页数: 16页
• 分类号: R73

摘要

This paper studies the problem of recovering low-rank tensors, and the tensors are corrupted by both impulse and Gaussian noise. The problem is well accomplished by integrating the tensor nuclear norm and the l1-norm in a unified convex relaxation framework. The nuclear norm is adopted to explore the low-rank components and the l1-norm is used to exploit the impulse noise. Then, this optimization problem is solved by some augmented-Lagrangian-based algorithms. Some preliminary numerical experiments verify that the proposed method can well recover the corrupted low-rank tensors.