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Convexity in Source Separation : Models, geometry, and algorithms

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5 Author(s)
McCoy, M.B. ; Comput. & Math. Sci., California Inst. of Technol., Pasadena, CA, USA ; Cevher, V. ; Quoc Tran Dinh ; Asaei, A.
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Source separation, or demixing, is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separation problems, from interference cancellation to background subtraction, blind deconvolution, and even dictionary learning. Despite the recent progress in each of these applications, advances in high-throughput sensor technology place demixing algorithms under pressure to accommodate extremely high-dimensional signals, separate an ever larger number of sources, and cope with more sophisticated signal and mixing models. These difficulties are exacerbated by the need for real-time action in automated decision-making systems.

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Signal Processing Magazine, IEEE  (Volume:31 ,  Issue: 3 )