2013/09/28 by Michael B. McCoy, Joel A. Tropp, McCoy, Michael B. +1 · 1 citation
Computer Science · Engineering · Mathematics · #60D05 #90C25 #94A15 #94B75 #Blind Source Separation Techniques #FOS: Computer and information sciences #FOS: Mathematics #Image and Signal Denoising Methods #Information Theory (cs.IT) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #cs.IT #math.IT #math.OC #msc:60D05 #msc:90C25 #msc:94A15 #msc:94B75
paper · pdf · doi:10.48550/arxiv.1309.7478
arxiv created 2013/09/28 · openalex publication_date 2013/09/28 · arxiv updated 2013/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Demixing is the problem of identifying multiple structured signals from a superimposed, undersampled, and noisy observation. This work analyzes a general framework, based on convex optimization, for solving demixing problems. When the constituent signals follow a generic incoherence model, this analysis leads to precise recovery guarantees. These results admit an attractive interpretation: each signal possesses an intrinsic degrees-of-freedom parameter, and demixing can succeed if and only if the dimension of the observation exceeds the total degrees of freedom present in the observation.