An optimal condition of robust low-rank matrices recovery
by Jianwen Huang; Sanfu Wang; Jianjun Wang; Feng Zhang; Hailin Wang; Jinping Jia
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 21, No. 1, 2021

Abstract: In this paper we investigate the reconstruction conditions of nuclear norm minimisation for low-rank matrix recovery. We obtain a sufficient condition to guarantee the robust reconstruction and exact reconstruction of all r-rank matrices from linear measurements via nuclear norm minimisation. Furthermore, we not only show that our result could return to previous result, but also demonstrate that the gained upper bounds concerning the recovery error are better. Moreover, we prove that the restricted isometry property condition is sharp. Besides, the numerical experiments are conducted to reveal the nuclear norm minimisation method is stable and robust for the recovery of low-rank matrix.

Online publication date: Fri, 19-Nov-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com