Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 57, Issue 11, Pages 4378-4390Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2009.2025154
Keywords
Atomic decomposition; blind source separation (BSS); sparse component analysis (SCA); sparse decomposition; sparse source separation
Categories
Funding
- Iran NSF (INSF) [86/994]
- Iran Telecom Research Center (ITRC)
- Center for International Research and Collaboration (ISMO)
- French embassy
Ask authors/readers for more resources
We present a Bayesian approach for Sparse Component Analysis (SCA) in the noisy case. The algorithm is essentially a method for obtaining sufficiently sparse solutions of underdetermined systems of linear equations with additive Gaussian noise. In general, an underdetermined system of linear equations has infinitely many solutions. However, it has been shown that sufficiently sparse solutions can be uniquely identified. Our main objective is to find this unique solution. Our method is based on a novel estimation of source parameters and maximum a posteriori (MAP) estimation of sources. To tackle the great complexity of the MAP algorithm (when the number of sources and mixtures become large), we propose an Iterative Bayesian Algorithm (IBA). This IBA algorithm is based on the MAP estimation of sources, too, but optimized with a steepest-ascent method. The convergence analysis of the IBA algorithm and its convergence to true global maximum are also proved. Simulation results show that the performance achieved by the IBA algorithm is among the best, while its complexity is rather high in comparison to other algorithms. Simulation results also show the low sensitivity of the IBA algorithm to its simulation parameters.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available