Estimating the Mixing Matrix in Sparse Component Analysis Based on Converting a Multiple Dominant to a Single Dominant Problem

Abstract : We propose a new method for estimating the mixing matrix, A, in the linear model , 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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https://hal.archives-ouvertes.fr/hal-00173370
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Submitted on : Wednesday, September 19, 2007 - 4:27:33 PM
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Nima Noorshams, Massoud Babaie-Zadeh, Christian Jutten. Estimating the Mixing Matrix in Sparse Component Analysis Based on Converting a Multiple Dominant to a Single Dominant Problem. 7th International Conference, ICA 2007, Jul 2007, London, United Kingdom. pp.397-405. ⟨hal-00173370⟩

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