Work

Robust network compressive sensing

Year: 2014

Type: conference-paper

Abstract: Networks are constantly generating an enormous amount of rich diverse information. Such information creates exciting opportunities for network analytics. However, a major challenge to enable effective network analytics is the presence of missing data, measurement errors, and anomalies. Despite significant work in network analytics, fundamental issues remain: (i) the existing works do not explicitly account for anomalies or measurement noise, and incur serious performance degradation under significant noise or anomalies, and (ii) they assume network matrices have low-rank structure, which may not hold in reality.

Source: ACM Annual International Conference on Mobile Computing and Networking (MobiCom)

DOI: 10.1145/2639108.2639129

Authors: Yi-Chao Chen, Lili Qiu, Yin Zhang, Guangtao Xue, Zhenxian Hu

Institutions: The University of Texas at Austin, Shanghai Jiao Tong University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green