An inexact continuation accelerated proximal gradient algorithm for low n-rank tensor recovery

The low n-rank tensor recovery problem is an interesting extension of the compressed sensing. This problem consists of finding a tensor of minimum n-rank subject to linear equality constraints and has been proposed in many areas such as data mining, machine learning and computer vision. In this pape...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of computer mathematics Ročník 91; číslo 7; s. 1574 - 1592
Hlavní autoři: Liu, Huihui, Song, Zhanjie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 03.07.2014
Taylor & Francis Ltd
Témata:
Algorithms
Artificial intelligence
Compressed
Data mining
low n-rank tensor
Mathematical analysis
Mathematical models
nuclear norm
Optimization
proximal gradient
Recovery
singular value decomposition
Splitting
tensor completion
Tensors
Vision systems
ISSN:0020-7160, 1029-0265
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The low n-rank tensor recovery problem is an interesting extension of the compressed sensing. This problem consists of finding a tensor of minimum n-rank subject to linear equality constraints and has been proposed in many areas such as data mining, machine learning and computer vision. In this paper, operator splitting technique and convex relaxation technique are adapted to transform the low n-rank tensor recovery problem into a convex, unconstrained optimization problem, in which the objective function is the sum of a convex smooth function with Lipschitz continuous gradient and a convex function on a set of matrices. Furthermore, in order to solve the unconstrained nonsmooth convex optimization problem, an accelerated proximal gradient algorithm is proposed. Then, some computational techniques are used to improve the algorithm. At the end of this paper, some preliminary numerical results demonstrate the potential value and application of the tensor as well as the efficiency of the proposed algorithm.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:0020-7160
1029-0265
DOI:10.1080/00207160.2013.854881