Title :
Estimating the mixing matrix in Sparse Component Analysis (SCA) using EM algorithm and iterative Bayesian clustering
Author :
Zayyani, H. ; Babaie-Zadeh, M. ; Jutten, C.
Author_Institution :
Dept. of Electr. Eng., Sharif Univ. of Technol., Tehran, Iran
Abstract :
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.
Keywords :
blind source separation; compressed sensing; expectation-maximisation algorithm; mixture models; sparse matrices; EM algorithm; EM-LMM method; EM-Laplacian mixture model method; SCA; expectation-maximization algorithm; iterative Bayesian clustering; maximum likelihood estimates; mixing matrix estimation; sparse component analysis; Abstracts; Accuracy; Bayes methods; Ice; Manganese;
Conference_Titel :
Signal Processing Conference, 2008 16th European
Conference_Location :
Lausanne