False data injection attacks against state estimation in electric power grids
Proceedings of the 16th ACM conference on Computer and communications security
Image compression and recovery through compressive sampling and particle swarm
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
BasisDetect: a model-based network event detection framework
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
False data injection attacks against state estimation in electric power grids
ACM Transactions on Information and System Security (TISSEC)
Fractal pursuit for compressive sensing signal recovery
Computers and Electrical Engineering
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We introduce greedy basis pursuit (GBP), a new algorithm for computing sparse signal representations using overcomplete dictionaries. GBP is rooted in computational geometry and exploits equivalence between minimizing the l1-norm of the representation coefficients and determining the intersection of the signal with the convex hull of the dictionary. GBP unifies the different advantages of previous algorithms: like standard approaches to basis pursuit, GBP computes representations that have minimum l1-norm; like greedy algorithms such as matching pursuit, GBP builds up representations, sequentially selecting atoms. We describe the algorithm, demonstrate its performance, and provide code. Experiments show that GBP can provide a fast alternative to standard linear programming approaches to basis pursuit.