Source Estimation in Noisy Sparse Component Analysis

Abstract : In this paper, a new algorithm for Sparse Component Analysis (SCA) in the noisy underdetermined case (i.e., with more sources than sensors) is presented. The solution obtained by the proposed algorithm is compared to the minimum l1 -norm solution achieved by Linear Programming (LP). Simulation results show that the proposed algorithm is approximately 10 dB better than the LP method with respect to the quality of the estimated sources. It is due to optimality of our solution (in the MAP sense) for source recovery in noisy underdetermined sparse component analysis in the case of spiky model for sparse sources and Gaussian noise.
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Contributor : Christian Jutten <>
Submitted on : Wednesday, September 19, 2007 - 3:47:04 PM
Last modification on : Tuesday, July 9, 2019 - 1:21:27 AM
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  • HAL Id : hal-00173337, version 1


Hadi Zayyani, Massoud Babaie-Zadeh, Christian Jutten. Source Estimation in Noisy Sparse Component Analysis. 15th International Conference on Digital Signal Processing, DSP 2007, Jul 2007, Cardiff, United Kingdom. pp.219-222. ⟨hal-00173337⟩



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