Paper Info
Title
Low-n-rank tensor recovery based on multi-linear augmented Lagrange multiplier method
Abstract
The problem of recovering data in multi-way arrays (i.e., tensors) arises in many fields such as image processing and computer vision, etc. In this paper, we present a novel method based on multi-linear n-rank and @?"0 norm optimization for recovering a low-n-rank tensor with an unknown fraction of its elements being arbitrarily corrupted. In the new method, the n-rank and @?"0 norm of the each mode of the given tensor are combined by weighted parameters as the objective function. In order to avoid relaxing the observed tensor into penalty terms, which may cause less accuracy problem, the minimization problem along each mode is accomplished by applying the augmented Lagrange multiplier method. In experiments, we test the influence of parameters on the results of the proposed method, and then compare with one state-of-the-art method on both simulated data and real data. Numerical results show that the method can reliably solve a wide range of problems at a speed at least several times faster than the state-of-the-art method while the results of the method are comparable to the previous method in terms of accuracy.
Year
DOI
Venue
2013
10.1016/j.neucom.2012.03.039
Neurocomputing
Keywords
Field
DocType
novel method,accuracy problem,augmented lagrange multiplier method,minimization problem,previous method,new method,low-n-rank tensor,low-n-rank tensor recovery,observed tensor,multi-linear augmented lagrange multiplier,state-of-the-art method
Minimization problem,Mathematical optimization,Tensor,Pattern recognition,Image processing,Algorithm,Augmented Lagrangian method,Augmented lagrange multiplier method,Artificial intelligence,Mathematics
Journal
Volume
ISSN
Citations 
119,
0925-2312
16
PageRank 
References 
Authors
0.59
12
6
Name
Order
Citations
PageRank
Huachun Tan113210.03
Bin Cheng2160.59
Jianshuai Feng3302.03
Guangdong Feng4241.82
Wuhong Wang59413.30
Yu Jin Zhang6127293.14