Abstract
We propose a new method for estimating the mixing matrix, A, in the linear model \({\textbf{x}}(t)={\textbf{A}} \textbf{s}(t), t=1,\dots,T\), 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.
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Noorshams, N., Babaie-Zadeh, M., Jutten, C. (2007). Estimating the Mixing Matrix in Sparse Component Analysis Based on Converting a Multiple Dominant to a Single Dominant Problem. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_50
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DOI: https://doi.org/10.1007/978-3-540-74494-8_50
Publisher Name: Springer, Berlin, Heidelberg
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