Mobility

Recovery of Low-Rank Plus Compressed Sparse Matrices With Application to Unveiling Traffic Anomalies

Date Added: May 2012
Format: PDF

Given the superposition of a low-rank matrix plus the product of a known fat compression matrix times a sparse matrix, the goal of this paper is to establish deterministic conditions under which exact recovery of the low-rank and sparse components becomes possible. This fundamental identifiability issue arises with traffic anomaly detection in backbone networks, and subsumes compressed sensing as well as the timely low-rank plus sparse matrix recovery tasks encountered in matrix decomposition problems. Leveraging the ability of L1- and nuclear norms to recover sparse and low-rank matrices, a convex program is formulated to estimate the unknowns.