Tensor completion using total variation and low-rank matrix factorization

E-resources

Peer reviewed

Tensor completion using total variation and low-rank matrix factorization

Ji, Teng-Yu; Huang, Ting-Zhu; Zhao, Xi-Le; Ma, Tian-Hui; Liu, Gang

Information sciences, 01/2016, Volume: 326

Journal Article

In this paper, we study the problem of recovering a tensor with missing data. We propose a new model combining the total variation regularization and low-rank matrix factorization. A block coordinate decent (BCD) algorithm is developed to efficiently solve the proposed optimization model. We theoretically show that under some mild conditions, the algorithm converges to the coordinatewise minimizers. Experimental results are reported to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme.

Keep searching

Author

Ji, Teng-Yu | Huang, Ting-Zhu | Zhao, Xi-Le | Ma, Tian-Hui | Liu, Gang

Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.

Year	Impact factor		Edition		Category		Classification
Year	JCR	SNIP	JCR	SNIP	JCR	SNIP	JCR	SNIP

Links to authors' personal bibliographies	Links to information on researchers in the SICRIS system

Source: Personal bibliographies and: SICRIS

Upload image

Shelf entry

Adding material to shelf was successful.

Adding material to shelf failed.

It was not necessary to add the material to the shelf.

Permalink

E-mail

Impact factor

Select the library membership card:

DRS, in which the journal is indexed

Citations

Theme