Estimating the mixing matrix in Sparse Component Analysis (SCA) using EM algorithm and Iteratice bayesian clustering

Hadi Zayyani (1) , Massoud Babaie-Zadeh (1) , Christian Jutten (2)

1 EE department of SUT [Tehran] - Department of Electrical Engineering [Tehran]
2 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique

In this paper, we focus on the mixing matrix estimation which is the first step of Sparse Component Analysis. We propose a novel algorithm based on Expectation- Maximization (EM) algorithm in the case of two-sensor set up. Then, a novel iterative Bayesian clustering is applied to yield better results in estimating the mixing matrix. Also, we compute the Maximum Likelihood (ML) estimates of the elements of the second row of the mixing matrix based on each cluster. The simulations show that the proposed method has better accuracy and less failure than the EM-Laplacian Mixture Model (EM-LMM) method.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Hadi Zayyani, Massoud Babaie-Zadeh, Christian Jutten. Estimating the mixing matrix in Sparse Component Analysis (SCA) using EM algorithm and Iteratice bayesian clustering. EUSIPCO 2008 - 16th European Signal Processing Conference, Aug 2008, Lausanne, Switzerland. 5 p. ⟨hal-00315889⟩