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Estimating the mixing matrix in sparse component analysis (SCA) using em algorithm and iterative bayesian clustering

Zayyani, H ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. Publisher: 2008
  3. Abstract:
  4. 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. copyright by EURASIP
  5. Keywords:
  6. Bayesian clustering ; EM algorithms ; Expectation maximization ; Mixing matrix ; Mixture model ; Novel algorithm ; Sparse component analysis ; Algorithms ; Computer simulation ; Mixing ; Signal processing ; Estimation
  7. Source: 16th European Signal Processing Conference, EUSIPCO 2008, Lausanne, 25 August 2008 through 29 August 2008 ; 2008 ; 22195491 (ISSN)
  8. URL: https://ieeexplore.ieee.org/document/7080331