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.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4666 LNCS, pp. 397–405). Springer Verlag. https://doi.org/10.1007/978-3-540-74494-8_50
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