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Estimating the mixing matrix in sparse component analysis based on converting a multiple dominant to a single dominant problem

Noorshams, N ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-540-74494-8_50
  3. Publisher: Springer Verlag , 2007
  4. Abstract:
  5. We propose a new method for estimating the mixing matrix, A, in the linear model x(t) = As(t),t = 1,...,T, for the problem of underdetermined Sparse Component Analysis (SCA). Contrary to most previous algorithms, there can be more than one dominant source at each instant (we call it a "multiple dominant" problem). The main idea is to convert the multiple dominant problem to a series of single dominant problems, which may be solved by well-known methods. Each of these single dominant problems results in the determination of some columns of A. This results in a huge decrease in computations, which lets us to solve higher dimension problems that were not possible before. © Springer-Verlag Berlin Heidelberg 2007
  6. Keywords:
  7. Algorithms ; Computational methods ; Linear equations ; Problem solving ; Multiple dominant problems ; Single dominant problems ; Sparse Component Analysis (SCA) ; Independent component analysis
  8. Source: 7th International Conference on Independent Component Analysis (ICA) and Source Separation, ICA 2007, London, 9 September 2007 through 12 September 2007 ; Volume 4666 LNCS , 2007 , Pages 397-405 ; 03029743 (ISSN); 9783540744931 (ISBN)
  9. URL: https://dl.acm.org/doi/10.5555/1776684.1776737