Publication

Convexity in source separation: Models, geometry, and algorithms

2013
Journal Articles

Abstract

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.

Official source

https://infoscience.epfl.ch/entities/publication/73c9df21-d7ae-4aec-97a4-c87870717800

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2013
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/73c9df21-d7ae-4aec-97a4-c87870717800

About this result

Related concepts (23)

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Related publications (1)

Convexity in source separation: Models, geometry, and algorithms

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Greedy Dictionary Selection for Sparse Representation